wp6 guidelines rockfall and forecast systems -...

PP3

WP6 guidelines

Rockfall and Forecast systems

Coordinator PP3

In collaboration with PP4 & PP10

PARAmount page 2 of 84

Authors of the WP6 guidelines rockfall

Italy

V. Larcher

Office of Geology and Building Material Testing

Autonomous Province of Bolzano-South Tyrol

Bolzano, Italy

C. Strada

Office of Geology and Building Material Testing

Autonomous Province of Bolzano-South Tyrol

Bolzano, Italy

S. Simoni

Mountain-eering srl - Spin off Trento University

Bolzano, Italy

R. Pasquazzo

Geologia, Geotecnica, Ambiente

Trento, Italy

G. Zampedri

Geological Service of the Autonomous Province of Trento

Trento, Italy

France

F. Berger

National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA)

Grenoble, France


CONTENTS

1 INTRODUCTION 5

1.1 PURPOSE OF THE REGIONAL SCALE ANALYSIS 5 1.2 PURPOSE OF A DETAILED SCALE ANALYSIS 6 1.3 THE CHOICE OF THE APPROPRIATE SOFTWARE 6

2 REGIONAL SCALE METHODOLOGIES 7

2.1 METHODOLOGIES BASED ON DEM 7 2.1.1 EMPIRICAL METHODS 7 2.1.2 THE CONCEPT OF THE ENERGY LINE ANGLE 8 2.1.3 FORESTRY PLANNING STRATEGY OF THE PROVINCE OF BOLZANO 31 2.2 PSEUDO-DETERMINISTIC METHODOLOGIES 34

3 DETAILED SCALE METHODOLOGIES 36

3.1 ROCKYFOR3D 36 3.1.1 DESCRIPTION 36 3.1.2 FIELD INVESTIGATIONS 36 3.1.3 PREPARATION OF INPUT DATA 37 3.1.4 ADVANTAGES/DISADVANTAGES 37 3.2 ROTOMAP 38 3.2.1 DESCRIPTION 38 3.2.2 FIELD INVESTIGATIONS 38 3.2.3 PREPARATION OF INPUT DATA 38 3.2.4 ADVANTAGES/DISADVANTAGES 39 3.3 GEOROCK 2D 40 3.3.1 DESCRIPTION 40 3.3.2 FIELD INVESTIGATIONS 40 3.3.3 PREPARATION OF INPUT DATA 40 3.3.4 ADVANTAGES/DISADVANTAGES 41 3.4 ROCFALL (ROCKSCIENCE) 42 3.4.1 DESCRIPTION 42 3.4.2 FIELD INVESTIGATION 42 3.4.3 PREPARATION OF INPUT DATA 42 3.4.4 ADVANTAGES/DISADVANTAGES 43 3.5 ROCKFALL (DR. SPANG) 44 3.5.1 DESCRIPTION 44 3.5.2 FIELD INVESTIGATION 44 3.5.3 PREPARATION OF INPUT DATA 44 3.5.4 ADVANTAGES/DISADVANTAGES 45


4 A COMPARISON BETWEEN ROCKFALL MODEL PERFORMANCES 46

4.1 APPLICATION OF ROCKYFOR3D AT REGIONAL SCALE 46 4.2 APPLICATION OF ROCKYFOR3D TO SOME CASE STUDIES 46 4.2.1 INPUT DATA (ROCKYFOR3D) 46 4.3 APPLICATION OF ROCFALL TO SOME CASE STUDIES 49 4.3.1 INPUT DATA (ROCFALL) 49

5 FIELD INVESTIGATIONS 51

6 MONITORING SYSTEMS/EARLY WARNING SYSTEMS 53

6.1 MONITORING SYSTEMS FOR ROCKFALL 53 6.2 CONTROL OF EXISTING PROTECTION MEASURES (CADASTRES OF PROTECTION MEASURES) 54 6.2.1 PARAMOUNT DATABASE 55 6.2.2 PREDICTION OF THE MODEL FAILURE AND BARRIER CAPACITY 58 6.2.3 FE MODELS OF FLEXIBLE BARRIER TYPE 64 6.2.4 NUMERICAL MODELS: TOWARD THE ACTUAL BARRIER 68 6.3 MONITORING SYSTEMS : THE EXPERIENCE OF FORTE BUSO, SS50, TRENTINO, ITALY 76 6.3.1 DESCRIPTION OF THE AREA 76 6.3.2 SENSORS AND MONITORING 78

7 BIBLIOGRAPHY 83


1 Introduction Rockfalls are common in mountain areas and represent a serious threat due to their high propagation velocity that, independently from the volume involved, can be extremely dangerous to buildings, roads and people. Therefore, it is necessary to preliminarily identify those areas most prone to this type of process, in order to pursue a territorial planning with consciousness of hazards and risks. Rockfall hazard analysis over wide territories is anyway rather difficult, because many variables, which are difficult to identify at that scale, have to be taken into account (i.e., fracturing of rock masses, presence of water etc.). Hence there is a need for identifying methodologies capable of reproducing complex processes that are involved in rockfall occurrence and propagation and to preliminarily identifying the areas most susceptible to this type of hazard. (Piacentini & Soldati 2008). Rockfall activity depends on geological, tectonic and topographical factors, in addition, rockfall processes are also sensitive to the meteorological conditions. During extreme precipitation events, a general increase of rockfall activity is observed. At altitudes around the 0°celsius amplitude the activity of rockfall processes is driven by changes between temperatures above and under the freezing temperature. Therefore, the expected climatic changes also affect rockfall processes. A special phenomenon of climate change effects influencing the rockfall activity is the degradation of permafrost. Therefore, rockfall process areas of which the starting points are located in rock faces underlying permafrost conditions have to be considered as climate-sensitive. (CLISP Project, A. Zischg, Abenis AG).

Figure 1. Rock fall events in the Province of Bolzano

1.1 Purpose of the regional scale analysis Steep rock cliffs can be affected by rockfalls that, due to their high velocity, can be very dangerous, independently from the volumes involved. This type of landslide can cause relevant damages to built-up areas and threaten human lives. Therefore it is of paramount importance to predict their run-out for a correct land management. This is a rather difficult task, particularly at a regional scale, because the most well-known methods are applicable only in small territories and generally along pre-defined fall profiles that do not contemplate lateral diffusion. The advantage of regional scale analysis is mainly the studies to achieve a cost-effective analysis methodology to be applied over wide territories, which did not necessitate a relevant amount of input data and could lead anyhow to reliable results.

The kinematic models, in spite of uncertainties related to the choice of the motion parameters, have the advantage to allow simulations that reproduce the landslide behavior, calculating trajectories, velocities and kinetic energies of blocks during their motion, which are fundamental for territorial planning and for the design of mitigation works (Piacentini & Soldati 2008).


1.2 Purpose of a detailed scale analysis Detailed scale software can be useful tools to asses hazards posed by rockfalls. In fact, they are a useful planning instrument/tool, to define the local hazard level on detailed scale. For planning new protection measures a good simulation software can give an objective assessment of energies and bounce heights. Obviously only correct investigated field data and a sufficient knowledge about calculations within the software give a comprehensive and reliable result for the process under investigation. In summary, a detailed analysis is the basis for a good planning of protection measures.

1.3 The choice of the appropriate software The choice of the appropriate simulation program is not a trivial issue. First of all the objective of the analysis has to be established. The choice has to be made, according to the aim one wants to achieve.

For hazard zone mapping a 3D software can be very helpful, since hazard zones can easily be delimited according to an distribution of energies through the area of interest. Nevertheless, additional check with 2D software can provide more details on the given situation.

So far 2D software proved to be more appropriate for designing purposes (e.g. new protection measures) in that kinetic energy on a certain hazard zone can be computed more in detail with respect to a 3D software. On the contrary, a 3D software, e. g. RockyFor3D, is definitely useful to check the length and height of the designed protection measure.


2 Regional Scale Methodologies

2.1 Methodologies based on DEM Different numerical models aiming at reproducing the process of rock falling can be used to analyze how blocks

move along a slope and, consequently, to estimate the run-out distance.

In practice, different model types are used:

1D/2D empirical models based on topographic parameters;

1D models calculating the energy of rock fall processes;

2D/3D models calculating the energy of rock fall processes;

The models with a high definition output require usually very detailed input datasets such as dimension (in x, y

and z axis of the block), volume, weight, geology, characteristics of fractures, exact position of releasing points,

vegetation and distribution of trees and their diameter in the run-out area, the energy dissipation by

cushioning of blocks on ground and much more. Models with a minor requirement of input data useable on the

Alpine scale are based on empirical relationships between topographic characteristics and run-out distances.

These models allow an easy and robust use on wide areas but do not allow to calculate the velocity of blocks or

the impact forces to endangered settlements. (from CLISP Project, A. Zischg, Abenis AG

2.1.1 Empirical methods

(Onofri & Candian, 1979; Heinimann& alii, 1998; Jaboyedoff & Labiouse, 2003; Piacentini, 2005)

Figure 2. Outline of run-out zones obtained through the three-dimensional empirical model (Piacentini D. & Strada C., 2006)

They consider dissipation energy proportional to maximum run-out distance. It is a three dimensional empirical method. Referring to the simulation angles proposed by the cited authors, the probable fall trajectories have been assessed and, among those, the ones that could better suite the specific morphological features of the studied areas have been selected. In particular, the possible maximum run-out zones have been calculated, through trajectories defined by angles. These angles define three different zones of possible propagation with reference to block size and slope characteristics.


2.1.2 The concept of the Energy Line Angle

The first step for elaborating an efficient risk prevention policy is to provide to experts and decision makers a clear and global vision of what could potentially happen in the territory that they manage. This territorial overview on potential geohazards can only be done at a global scale which is commonly the regional ones. Such information can support risk managers and decision makers in determining where local scale expertise is needed and to fix priorities in term of technical interventions and funds allocations.

The general principle of hazard mapping procedure is 1) to determine the localization of the hazard potential and active release areas, and 2) to estimate from these release areas what are the maximal possible run-out distances.

Working at a regional scale means that modelling tools, for which high resolution data and field surveys are needed, are not directly usable. Due to the resolution and the accuracy of the data available at a regional scale, the use of process based 3d propagation models needs to aggregate and simplify the input parameters and so to deteriorate the domain of validity of the results obtained with such models. This kind of models is also time consuming and needs high storage numerical facilities. So, for providing a robust and usable regional analysis the experts have to use models needing the lowest amount of input data, data which should be easily available without specific field investigations. Probabilistic empirical models based on the analysis of past events database are clearly answering to these needs.

The advantage of such models is that for identifying potential release and propagation zones they do not required any meteorological or geological data. Effectively these models are only based on topographic criteria and so the only input datum is a digital terrain model (DTM). Nowadays DTM are available for all the alpine space countries and so empirical models can be easily used for a first hazard geographical assessment and pre-mapping.

Historically, the concept used for elaborating the first empirical model has been established by Heim in 1932 for rockfalls hazard. This concept is the one of the Energy Line Angle (ELA). This concept states that the maximal run out distance of a rockfall is corresponding to the point resulting of the graphical intersection between the horizontal plan and a virtual line (the energy line) starting at the release point and having a specific slope angle (°). The following figure illustrates this concept.

Figure 3. The rockfalls Energy line Angle concept

Heim has also proved that the determination of the run out distance can be done using two different angle of energy line. The calculation can be done 1) using the geometric angle which is calculated using the horizontal projection of the direct slope line between the release point and stopping one, or 2) using the travel angle


which is calculated from the length of the horizontal projection of the line corresponding to the water flow direction along the slope. The travel angle is always flatter than the geometric ones, because the distance derived from the water flow direction is longer than the one calculated along the direct slope and so the ratio

of height and horizontal length is smaller (cf. Figure 4).

Figure 4. Difference between the Energy Line geometric angle (in red) and the Energy Line travel angle (in blue).

Since 1932 all published field observations shows that the magnitude of the Energy Line Angle is within a certain range and above a certain limits with little variation (see below the chapter on rockfalls zoning). Therefore, it’s possible to get a realistic order of magnitude of the rockfalls run-out length from a known release point.

The Energy Line Angle concept of Heim has been adapted by Scheller in 1970 in order to calculate the maximal

run out point using the foot of the cliff and not its top. This author has so defined the shadow angle. Figure 5

presents these two angles and the formula to be used for calculating the Energy Line Angle (Δz has to be adapted for the calculation of the shadow angle). Meissl in 1998 has determined that the difference between the geometric angle and the travel ones is less than 1 degree. The geometric angle is the easiest to determine both in the field with an inclinometer or using a DTM.


Figure 5. The difference between the Energy Line Angle (β) and the shadow ones (α).

Dorren and Berger (2003) have confirmed the possibility of estimating the maximal speed of a rock using the

maximal difference of altitude between the energy line angle and the slope profile (cf .Figure 6).

Figure 6. How to determine the maximal speed of a rock using the Energy line Angle


The Energy Line Angle concept is perfectly adapted to a regional scale assessment if past rockfalls events data base is available. For each past event the energy line angle can be calculated and so a statistical model can be calibrated.

This simple concept based on the principle of the friction angle has been also tested for predicting snow avalanche maximal run out distance. The first author who has transposed this concept to snow avalanche problematic is Körner in 1976. In 1979 Lied has proposed, for the Norway topographic conditions, the first snow avalanche maximal run out distance predictive model derived from the energy line angle concept of Heim. Since then, the protocol used by this author for building up his model is the international reference for calibrating snow avalanche maximal run out distance empirical models. This protocol is presented in the next chapter. As for rockfalls the calibration of energy line snow avalanche model needs a paste event data base for building up the statistical law to be used.

The simplicity of the ELA concept and the fact that the statistical laws used by the ELA models are calibrated using past events data, provide hazard maps useful for risk managers who can have a first and preliminary estimation of potential run out distances and geographical sectors for which local scale analysis need to be performed. Preliminary hazard mapping using the ELA concept can be quickly carried out using terrain data and grid calculation modulus of GIS tools.

Within the Paramount project, the French partner IRSTEA has developed two regional scale models based on the ELA concept, the first one AvalforLIN is dedicated to snow avalanche hazard mapping and the second one RockforLIN to rockfalls hazard mapping. These models are able to display the geographical areas potentially affected by these hazards, and output the effect of the forest areas having a potential protection role against these two natural hazards (provided the forest map is available for the study area). As these models are dedicated to a regional scale mapping, they can be used both for risk prevention and forest management strategic planning. The two main objectives of these models are to identify in a study area if human infrastructures are potentially endangered and if forest stands located above these infrastructure can provide a protective function.

The general principle for this mapping is based on answering to the following questions:

1. Where are the release areas?

2. What is the maximum propagation area envelope?

3. Is any human infrastructure located in the propagation area and if so, is it endangered?

4. Are any forest stands located in the release area and/or in the propagation area above the human infrastructure endangered?

If the answer to the third question is yes, then a specific analysis has to be conducted in order to assess the need for protective works. If the answer to the fourth question is yes, then these forest stands serve a protective function, and an investigation at a local scale is needed in order to qualify and/or quantify the efficiency of this protection.


Rockfalls hazard mapping with RockforLIN

RockforLIN is a 2D raster GIS model developed by IRSTEA which is able, depending on the available input data, to answer to the four questions expressed just above. The general flow chart of RockforLIN is the following:

Figure 7. The general flow chart of Rockfor

LIN


Depending on the objectives of the study, the input data needed are the followings:

Objectives

Input data : raster maps (the resolutions can be different but the minimal resolution is the one of the DTM)

DTM

Past event cadastres or any

geo-localized information

Human infrastructures

Forest map

Release zones mapping

Run-out zones mapping

Risk mapping

Protection forest mapping

Table 1. Input data data needed for using RockforLIN

The following maps illustrate the 3 main input data.

Figure 8. Map displaying the 25x25m resolution DTM of the French case study “département des Hautes-Alpes” (5549 km

2)


Figure 9. The map of the issues of the French case study “département des Hautes-Alpes”.

Figure 10. The forest map of the French case study “département des Hautes-Alpes”.


Rockfalls release areas mapping with RockforLIN

RockforLIN

can only be used if all potential rockfalls release points have been mapped. This mapping can be done via field surveys, aerial photography analysis, past events data base analysis or numerical processing on the DTM. Rockfor

LIN offers the possibility to perform a DTM analysis for identifying rockfalls potential release

areas. The assumption which has been taken is that the release areas depend only on the topographic conditions of the case study. Therefore a simple slope threshold can be applied to the slope surface raster (computed from the raster Digital Terrain Model [DTM]). The resolution and the accuracy of the DTM affect the results. At least a DTM resolution of 25x25m is necessary. According to the results of Dorren and Berger (2003), the equation used in Rockfor

LIN is:

α = 55 x RES-0.075

, where RES is the DTM resolution

All cells with values higher than the threshold value α are qualified as potential release zones for rockfalls. This equation has been established using a multi-resolution analysis of slope gradient in known existing rockfalls source area in five sites: France (1), Austria (2), Switzerland (1) and Lichtenstein (1). The table below gives the threshold values for determining rockfalls release area for DTM main resolutions.

DTM resolution/cell size [m] Threshold slope gradient [°]

1 55

5 49

10 46

25 43

Table 2. Threshold values for determining rockfalls release area for DTM main resolutions

Within RockforLIN

the user has to enter the DTM’s resolution and then 1) the slope threshold is automatically calculated and 2) the potential rockfalls release areas map is automatically produced. If the user wants to use with another slope threshold value this is possible and he has to enter the value he wants to work with. It’s important to keep in mind that the slope threshold value calculation using a DTM could generates local errors and that this way of calculation can only determine release areas with a minimal height. So the resulting map has to be validated in the fields.

Actually in a DTM the real slope is calculated as ratio between the elevation difference (between two neighboring cells) and the cell size; therefore if an additional falling height needs to be accounted for, the relative angle should not be considered in the threshold value. For example a slope of 43° in a 25x25m DTM can represent very different profiles, as expressed in the figure below:

Figure 11. Example of variation of release area configuration expressed with a slope threshold of 43°.


So in one cell the potential release vertical the DTM resolution) of the slope below the foot of the release area and, on the value of the slope angle

Figure 12. Parameters influencing the potential release vertical height.

The table below gives for 1x1, 10x10 and 25x25m DTM resolutions the potential Vertical Release Height (VRH)

with ≤ and slope length = DTM resolution.

DTM

Resolution

[m]

Slope threshold : [°]

1,00 55,00

Slope : [°]Vertical release height : VRH

[m]

0 1,43

5 1,34

10 1,25

15 1,16

20 1,06

25 0,96

30 0,85

35 0,73

40 0,59

45 0,43

50 0,24

55 0,00

DTM

Resolution

[m]

Slope threshold [°]

10,00 46,28


[m]

0 10,46

5 9,58

10 8,69

15 7,78

20 6,82

25 5,79

30 4,68

35 3,45

40 2,06

45 0,46

46,28 0,00

DTM

Resolution

[m]

Slope threshold [°]

25,00 43,20


[m]

0 23,48

5 21,29

10 19,07

15 16,78

20 14,38

25 11,82

30 9,05

35 5,97

40 2,50

43,2 0,00

Table 3. Value of the potential Vertical Release Height for 1x1, 10x10 and 25x25m DTM resolutions with ≤ and slope length = DTM resolution

The use of the angle threshold is well suited for vertical release height of: ≥23.5m in a DTM having a resolution of 25x25m, ≥10.5m in a DTM having a resolution of 10x10m, ≥1.5m in a DTM having a resolution of 1x1m. These values provide the application range for this method. For DTM resolutions lower than 1m, the slope gradient release areas method allows the expert to obtain a pre-map which can identify the potential release areas having a vertical height at minimum equal to the DTM resolution. So the expert has to check the validity of this pre-map for release area having a vertical height lowest than the DTM resolution. One possible solution is to use a lowest angle threshold. In this case some “false” release area can be detected and the validation of the pre-mapping by the expert is obligatory. This validation can be done by sampling check point in the field.

The following figure illustrates the results obtained with this method in the French case study of the Paramount project.


Figure 13. Map of rockfalls release zones obtained with Rockfor

LIN (French case study “ département des Hautes-Alpes”)

Rockfalls potential maximal run-out areas mapping with RockforLIN

From each of the identified potential release areas, RockForLIN

simulates the maximal probable propagation envelopes. For performing this calculation RockFor

LIN uses based on the Energy Line principle (Heim, 1932), and

on the use of a lateral spread off angle. The lateral spread off angle is used for characterized the potentiality of lateral deviation of a rock along the most probable trajectory path. The value of this angle has been fixed, using literature data (Dorren and Berger 2003, Jaboyedoff and Labiouse 2011), to 20°.

Concerning the choice for value of the Energy Line Angle, the same approach based on a state of the art has been initially used. The table below gives the available value in the literature of the Energy Line Angle.

Author/Source

Energy Line Angle

Between brackets the value for the geometrical angle

Minimum or interval of values

Average value

Shreve (1968) (26.57° - 38.66°) ---

Hsü (1975) 31° (32°) ---

Onofri & Candian (1979) 28.34° – 40.73°

(28,84 ° - 41,73°) ---

Grunder ( 1984) 32.6° - 33.4°

(33.1° - 34.4°) ---

Moser (1986) 33° - 42°

(34° - 43°) ---


Domaas(1985 in Toppe 1987) 32° (33°) ---

Mac ewen (1989) (30.96°) ≈ (31°)

Gerber (1994) 33°- 37°

(33.5° - 38°) ---

Meissl (1998) 29° – 47,5°

(29.5°- 48.5°) 38° (38°)

Heinimann et al. (1998) 33° - 37°

(33.5° - 38°) ---

Focardi & lotti (2001) 27° - 29°

(27.5° - 30°) ---

Ayala-carcedo et al. (2001) (29.1° - 38.9°) (31,9°)

Jaboyedoff & Labouise (2003) 32° (33°) ---

Jaboyedoff & Labouise (2011) (32,6° - 35,6°) 34°

Corominas et al. (2003) 26° - 54°

(27° 55°) ---

Dorren & Berger (2005,2006) 31.3° - 37°

(31.9° - 38 °) ---

Copons et al.(2009) site a (36.87° - 56,3°) ---

Copons et al.(2009) site b (28.81° - 42.0°) ---

Hutter et al. (2005) reduce scale experiments

(30° - 37°) ---

Scheidegger (1973) (29.68° - 39,69°) ---

Marquinez et al. (2002) site 1 (32.5° - 40.9°) (31.5° - 40.2°)

Marquinez et al. (2002) site 2 (29.4° -38.5°)

Antoniou & Lekkas (2009) (35°) ---

Deparis et al (2008) (31,61° - 47,20°) ---

Hyndman & Hyndman (2009) (33°) ---

Berger et al. (2009) with forest (27.67° - 33.88°) ---

Berger et al. (2009) without forest (31.32° - 37.86°) ---

Berger et al. (2009) reduce scale experiments

(32.57° - 48.99°) ---

Table 4. State of the Art on the Energy Line Angle values

A statistical analysis has been performed using the data provided in Table 4, the results of this analysis are

presented in Table 5.

Statistics Minimal Geometrical Angle Maximal Geometrical

Angle

Mean 31.14° 39.30°

Min 26.57° 30°

1st

quartile 29.45° 36,97°

2nd

quartile 31.61° 38.58

3rd

quartile 33° 41.80°

Max 36.87° 48.99°

Table 5. Statistical distribution of the published minimal and maximal geometrical Energy Line Angle


In parallel to this analysis, the French Northern Alps rockfalls data base has been used for a statistical analysis

of the observed values (see Table 6). Since 2012 for each event in the French Northern Alps a field survey has

been carried and the geometrical Energy Line Angle is measured.

Statistics Volume

(m3)

Energy Line Angle (°)

Centile Energy Line

Angle (°) Centile

Energy Line Angle (°)

Minimum 0.02 24.65 0.001% 24.65 4.00% 28.00

Maximum 200.00 58.42 4.00% 28.00 20.00% 32.00

Mean value 6.46 36.69 9.00% 30.00 40.00% 35.00

Standard deviation

18.88 5.54 35.00% 34.00 65.00% 38.00

Median 1.50 36,00

Total number of

events 194

Table 6. Results of the statistical analysis on the geometrical ELA in the French Northern Alps rockfalls data base

All these data have been used for defining a matrix of rockfalls propagation probabilities depending on the value of the geometrical Energy Line Angle (β). This matrix, given below, is the one used within RockFor

LIN for

run-out zone calculation.

Geometrical Energy Line

Angle thresholds

Probability of rockfalls

propagation

≥ 38° High

35°≤ <38° Medium

32°≤ < 35° Low

28° °≤ < 32° Very low but not null

Table 7. The geometrical ELA matrix of RockForLIN

Usually the two main maps used by the experts are the 32° and the 38° ones. The 32° represents the most probable maximum run-out zones without taking into account the role played by the forests present on the path and, at the opposite, 38° represents the probable run-out zone taking into account the optimal role of an ideal forest. So the value of 38° displays the potential maximal efficiency that forests can provide.

RockForLIN

also calculates a “reaching frequency” which corresponds to the number of release zone feeding one propagation cell. So the result can be expressed with an uniform color if the info mapped is only the envelop of the propagation area or with a color gradient if the info mapped is the reaching frequency.

The following maps illustrate the results obtained with this method in the different case studies of the Paramount project.


Figure 14. Rockfalls probable propagation zones map obtained with RockFor

LIN ,and using a 25x25m DTM, for the French

case study “ département des Hautes-Alpes”.


a b

Figure 15. a) Map of likely propagation area (in orange, in red the release areas) determined with an ELA of 32° and using a 25x25m DTM for the Queyras area (zoom of the map at the scale of the “département des Hautes-Alpes”- b) Map of likely propagation area (in orange, in red the release areas) determined with an ELA of 38° and using a 25x25m DTM for the Queyras area (zoom of the map at the scale of the “departement des Hautes-Alpes”.

Figure 16: Map of likely propagation area including the probable reaching frequency (in red the release areas) determined

with an ELA of 32° and using a 1x1m LiDAR DTM for Forte buso case study (Italy).


Figure 17: Map of likely propagation area including the probable reaching frequency (in red the release areas) determined with an ELA of 32° and using a 1x1m LiDAR DTM for Forte buso case study (Italy).


with an ELA of 38° and using a 1x1m LiDAR DTM for Forte buso case study (Italy).


Figure 19: Map of likely rockfall propagation area including the probable reaching frequency (in red the release areas)

determined with an ELA of 38° and using a 20x20m DTM for Tognazza Cavallazza case study (Italy).



with an ELA of 32° for the Austrian case study


with an ELA of 32° for Baca case study (Slovenia).


Validation of RockforLIN

The model RockForLIN

has been validated using 20 well documented past events. For each of them the maximal

run-out distance is at least included in the 32° ELA. Figure 22 displays an example for Saint Paul de Varces,

France. In this district a rockfall occurred on December 28th

, 2008. The observed trajectories fit the 38° ELA propagation zone (the versant is forested). RockFor

LIN has been able to identify the release area (circle in yellow

on the map and the photo) and the maximal run-out point (green circle on the map and the photo).

Figure 22: An example of validation of the results obtained with RockFor

LIN , the event of the 28/12/2008 in the district of

saint Paul de Varces in France.

It is very important to remind that the results obtained with RockForLIN

have always to be compare to the known events in the case study. If the observed run-out distances are longer than the ones calculated with RockFor

LIN then the value of the

ELA has to be adapted.

Rockfalls risk mapping with RockforLIN

The last step of a risk mapping is to determine if socio-economic issues are endangered by the natural hazard under investigation. Reliable information on the location of facilities is then required. At a regional scale, each of the Alpine Space countries can find this information in the geographic database of their respective national geographic institutes.

Usually these databases list and correspondently map all human infrastructures: public facilities, dwellings, industries, as well as communication, electrical, gas and water infrastructure, etc. According to their importance or their extent, all these items can be classified into protection priority levels. This ranking is not mandatory, but facilitates the definition of priority levels for specific protection actions, primarily depending on their importance and relevance to human activities, and secondly on the hazard intensity. Normally, the


priority ranking is decided in accordance with all actors involved in risk prevention policy of the study area (vulnerability map).

By combining this map with the rockfalls run-out envelope map, the potentially endangered infrastructures can be identified by selecting all the items located between release points and run-out envelopes. The map obtained includes the endangered issues and the associated release and run-out zones. If the vulnerability map is available then RockFor

LIN performs automatically this analysis by maps crossing.

The following maps illustrate different way of presenting the results of this risk analysis.

a b

Figure 23: a) Likely rockfalls 32° ELA propagation zones (in orange) and human infrastructures localization (violet) for the Queyras area (zoom of the map at the scale of the “département des Hautes-Alpes”- b) Likely rockfalls 38° ELA propagation zones (in orange) and human infrastructures localization (violet) for the Queyras area (zoom of the map at the scale of the “département des Hautes-Alpes”).


a

b

c

Figure 24: a) Map of endangered infrastructures using the rockfalls propagation map provided by RockForLIN

for the case study Mittewald in Italy a) 1x1m LiDAR DTM ;b) the 32° ELA rockfalls propagation zone including the reaching probability; c) identification of the sections of the road endangered and risk ranking using the reaching probability : section in purple = high level of risk, section in blue = medium level of risk, section in green-yellow = low level of risk, no color = no risk


Figure 25: Probable rockfalls propagation zones including the probable reaching frequency (in red the release areas) determined with an ELA of 32° and localization of the railway for Podbrdo case study (Slovenia).

Rockfalls protection forest mapping with RockforLIN

The protection forest mapping is the last step of the risk analysis than can be performed with RockForLIN

. The general principle is to intersect the map of endangered issues with the map of the geographical extension of forest stands. This forest map can be provided by National Forest Inventories or eventually available at the Forest Services. As the mapping is usually available at regional scale, the dendrometrical description of the forest stands is not required for the analysis. The information required is the surfaces covered by forest. Identification of forest stands potentially serving a protection function is then obtained by combining the endangered items map with the forest cover map, and by selecting all forested areas located above an endangered item and on/or between the associated release and run out zones. This selection is provided automatically by RockFor

LIN.

The map of potential protection forest areas has to be validated by field surveys. But before this, it can be used to define an area within which forest management dedicated to the improvement of the protection function needs to be applied. In other terms, this map defines the potential area of use of protection forest management guidelines.

The strength of this methodology lies in its ability to display the area within which forests are able to provide a protective function against rockfalls; often such areas are unknown having not been previously identified. A decrease in forest canopy in these protection forest areas could have dramatic consequences requiring adaptations to forest management to ensure the sustainability of this protective function.


The following maps present examples of the results of the potential protection forest mapping performed with RockFor

LIN.

Figure 26: Rockfalls potential protection forest map obtained with RockFor

LIN, and using a 25x25m DTM, for the French

case study “ département des Hautes-Alpes”.


Figure 27: Map of protection role of forest (in green) against rockfall potential obtained with RockFor

LIN , and using a

25x25m DTM, for the French case study Queyras (zoom of the map at the scale of the “département des Hautes-Alpes”)

RockForLIN is the property of IRSTEA, for any information contact:

[email protected].

mailto:[email protected]


2.1.3 Forestry planning strategy of the Province of Bolzano

D16 Approach D16 (Meissl, 1998)

This approach proposed by Meissl in 1998 calculates the segment of a single trajectory path from each pixel belonging to the delimitated rockfall release area to the next pixel downslope. Differently from the D8 approach (Orlandini et. al., 2003) also implemented in ArcGIS (flow direction), this approach minimizes the relative propagation error due to the small number of possible flow directions. The approach considers the 16 the next but one neighboring pixels instead of the 8 neighboring pixels of the D8 approach. For each of the 16 neighboring pixel the gradient f is calculated with respect to the central pixel S.

d

hhf a )(

hA … altitude of pixel S

h … altitude of neighboring pixel

Figure 28: 16 neighboring pixel considered for the calculation of the rock fall path (trajectory) after Meissl (1998)

The result of the procedure consists in determining the starting point and the endpoint of each segment of the

rockfall path. The benefit of considering more than 8 flow directions consists in limiting propagation errors on

slopes with a relatively constant exposition. This model has been widely used in the elaboration of hazard index

maps (Zischg et al. 2002).

The calculation of a 1D rockfall path (1 trajectory) does not allow considering lateral propagations or deviations

from the most suited path. Therefore, a Monte Carlo approach was used to simulate the most likely

possibilities of process propagations. The Monte Carlo approach used in this thematic means to compute

multiple paths for each pixel of the rockfall release area following the approach of Gamma (2000, random

walk). After each run (calculation of a single path) of the Monte Carlo simulation, the pixels of the DTM

overpassed by the first run are modified. By increasing the absolute height of these pixels for a certain amount,

the subsequent run computes another rock fall path accordingly to the modified DTM. The modification of the

DTM during the simulation requires an algorithm for the automatic detection and filling in of the “new” sinks

resulting from the modification of the DTM. The procedure stops when the constraints for the abort are met

(Figure 29).

A simpler version was used by the Province of Bolzano to derive a hazard index map for rockfall processes in areas characterized by permafrost. This model could also be used on an Alpine-wide scale and is described in the following. The run-out distance is calculated on the basis of the angle between the highest point of the rock face (potential releasing zone) and the lowest point of the deposition area (Pauschalgefälle). This approach can also be used in the elaboration of hazard index maps in wide areas (BUWAL 1998, Meissl 1997, Zischg et al. 2002) (Figure 30).

The procedure could be implemented either in a raster based approach in a vector lines approach. In the last case, the angle is calculated for each line segment.


Figure 29: Computation of subsequent rock fall paths during the Monte Carlo simulation (Zischg, A. – CLIPS Project).

Figure 30: Modelling the run-out distance

Delimitation of release zones

Depending on the content and format of the available topographic maps, the rockfall releasing zones can be identified from the rock face signatures of the topographic maps.

If available, the releasing zones could be derived from geologic maps in combination with a set of critical slope angles for the different lithographies.

If no geologic maps are available, the releasing zones can be derived from land cover maps in combination with a set of critical slope angles for the different land cover classes. In this case, the minimal slope inclination of each land cover class above which rockfall processes are possible, must be calibrated on representative test cases in the pilot regions

In case the focus is on investigating climate-sensitive rockfall areas, the procedure requires a permafrost

distribution map that allows to select the rockfall releasing zones in permafrost areas.


Effects of climate change within the modeling approach

Releasing areas in permafrost zones can be selected from the modeled rockfall releasing zones. The modeled

rock fall run-out areas from blocks starting in permafrost areas are considered as climate-sensitive areas i.e.

areas in which the activity of rockfall process is likely to be modified under future climate conditions.

Figure 31: Flowchart for the derivation areas prone by rockfall processes that are sensitive to the degradation of permafrost

Output of the procedure is a hazard index map identifying and delimiting the areas potentially endangered by

rockfall processes activated by permafrost degradation. From this map a subset of rockfall paths can be

selected as source in permafrost areas.

Figure 32: Example of a rock fall hazard index map.


2.2 Pseudo-deterministic methodologies

Database VISO

The need for prevention actions against rockfall olong road infrastructures in the Province of Bolzano induced the Road Service together with the Geological Service and the Department of Informatics to develop a tool capable of investigate and catalogue rockfall protection measures with the purpose of evaluating hazards along any given road stretch. Therefore the V.I.S.O. project (Viability Information System) was developed. V.I.S.O. consists of two main parts: an alphanumerical (VISO application) and a geometric database (SDE strata). VISO offers a module with a standardized interface and provides at the same time not only a connection to the GIS-system but also to the databases Oracle and the Microsoft Access archives. The V.I.S.O. tool offers the surveyor a way to quickly detect hazards due to landslide or toppling phenomena that characterizes a slope adjacent to any given road stretch. It also allows creating a priority list for intervention (new investments) and maintenance based on fundamental parameters like the hazard level of the slope (in the future it will become the risk). The development of a tool to link an index of vulnerability and exposition to every segment of the road network and finally to calculate the risk is still ongoing in collaboration with the University of Bologna DICAM (Department of infrastructure engineering) in the frame of the Project PARAmount.

SLOPE HAZARD LEVEL

The procedure to define the specific hazard level is based on the detection of 7 fundamental parameters:

Intensity of the landslide phenomenon;

Probability for the phenomenon to occur at the same point again;

Hazard level of the slope without protection systems;

General situation of protection systems;

Hazard level of the slope supplied with protection systems;

Vulnerability of the road segment;

Specific risk of the road segment.

These parameters need field work to define three major points:

Survey of the position (GPS or classical topographic methods) and of the characteristics of the protection system(s); this implies the identification of the type of protection measure and the determination of their geometrical features.

Detection of the intensity of the rock fall events that may occur on the slope and by assessing specific damping factors. The intensity of the event (G.E.I. - geological event intensity) is given by the sum of the following parameters: single block volume, greatest volume to be mobilized, state of decompression of the slope and structural situation of the rock face (orientation and spacing of discontinuities). The damping factors (S.C. - Slope Coefficient) are assigned through the definition of the slope angle, the morphology and the rebound of the slope, as well as the type and density of vegetation.

Survey of functional characteristics of the protection system; this includes its conservation state, its efficacy and its proper positioning related to the intensity and the geometry of the phenomena that may develop on the slope as defined in the previous step.

The assessment of the hazard level for a slope segment without any protection measurement is fundamentally based on the intensity of the phenomenon (S.E.I. slope event intensity), given by the sum of G.E.I. and S.C. parameters, and the probability of occurrence. The calculation of the probability of occurrence in V.I.S.O. method is based on the counting of every rock fall event (records of surveys, and/or technical reports archived at the Office for Geology and building materials testing of the Autonomous Province of Bolzano/Bozen) within the maximum time span of monitoring available (from 1998 onwards). The error bars depend clearly on the quality of event detection and the period of monitoring. The assessment of the hazard level to a slope segment with protection systems is given by crossing the hazard value for the slope without protection systems and the evaluation of the examined protection system.

THE ATTRIBUTION OF A HAZARD LEVEL TO A ROAD SEGMENT


A road at the base of a slope is affected by the hazards above which depend on the slope event intensity and the state of the protection systems as shown above. To calculate the hazard for a specific road sector, the arterial roads are subdivided into segments of homogeneous hazard level in this way: first the slope is divided into areas with the same slope event intensity (S.E.I). In the next step these portions of slope are intersected with the present protection system. Where there are no protections, the slope hazard is attributed directly to the road segment. Where protections are present, the remaining hazard below all protection system lines is attributed to the road segment below. To simplify the different GIS operations and statistical calculations every road segment is represented by its median point which gets all information of the entire segment.

Figure 33: Hazard level referred to a road segment

DRAFTING OF INTERVENTION PRIORITY LIST

To give every median point a priority of intervention, it is necessary to assign a numeric value at every point in the matrix slope event intensity (S.E.I.), vs. the probability of occurrence (Tr). This is made by the formula

3

...

9

550*

IESTrh ,

where α is a value that defines the functional characteristics of the protection systems. It ranges from -33.33 in the case the protection is as best as possible, to 0 in case the structures are not present or have no effect to + 11.11, where the protection does even aggravate the situation. After that each specific segment of an arterial road is given an index of intervention priority. This index is useful to to elaborate a maintenance plan of the protections, to schedule extraordinary repairs or replacement of protection systems and to target new mitigation measures.


3 Detailed scale methodologies In the following the experience built during the project in using several rock fall simulation software will be presented. Comments and experience from external consultants, collaborating in the project (geologists and engineers) were also taken into account and presented in a dedicated section (tables “Scientific Ability Criteria” and “Usability”).

3.1 RockyFor3D

3.1.1 Description

The software is developed by dr. Luuk K.A. Dorren and a team of contributors. It is distributed to members by the international association ecorisQ, the manual and detailed information can be found at www.ecorisq.org. Rockyfor3D calculates trajectories of individually falling blocks, over a 3D-topography. The model allows for soil and forest characteristics to be taken into account, as well as the characteristics of the falling rocks, in terms of block shape, material density, dimensions (up to 3 different diameters). Soil type characteristics allows for appropriate roughness and coefficients of restitution to be assigned to different portions of the ground, so that energy loss during rebound calculation can be accounted for. Trajectory are computed as results a of sequence of parabolic free falls a rebounds. Rebounds can occur both in case of impact against the ground or against a tree; it is treated as a partially inelastic collision, meaning that in case of a rebound against the ground, the block loses part of its energy, the soil is deformed and a penetration depth is computed. Direction changes are allowed using a dedicated algorithm, empirically derived from field observations. In case of impact against a tree, the energy loss is computed using an empirically derived algorithm, which accounts for the type of impact (frontal, lateral or scratch) and for the incoming velocity (module and direction). Nets can also be accounted for by setting the number, the position, the height and the energy of nets. The deterministic approach is complemented by a probabilistic method which allows to run simulations for a single rockfall (detaching from the same source cell) several times. The model outputs are given in terms of raster maps (ASCII files) of mean values and values related to the 95% confidence interval (assuming a Gaussian distribution).

3.1.2 Field investigations

Data collection in the field is of paramount importance. Data that need to be collected deals with soil type, in terms of roughness and stiffness of the impacted surface, source area, in terms of rock type and density, block shape (spherical, parallelepiped, ellipsoid, disc) and dimensions (through the definition of 3 diameters). Forest types (conifer and broadleaves) and net position must also be noted if the users want to include those elements in the analysis.

The procedure for data collection in the field mainly consists in 5 steps: firstly the source areas have to be identified (e.g. steep slopes, outcropping wall, overhanging section, etc.) and characterized defining the prevailing shape of the potentially falling blocks, the rock density and the block average dimensions; secondly, homogeneous soil type polygons have to be drawn to cover the entire study area. Each polygon must fall within one of the seven categories implemented in the code (see User Manual for details) and its perimeter possibly recorded via GPS. Thirdly, soil roughness has to defined for each polygon, in terms of probability that a falling rock will encounter a given height obstacle along its path. Probability are fixed to 70%, 20%, 10%; the user must identify the corresponding obstacle height in each polygon. As fourth step data on the forest has to be recorded. In particular the tree position (East ,North), the diameter at breast height (DBH) and the percentage of conifer; alternatively one can record the number of stems per hectare, the DBH mean and its standard deviation. Finally evidence of rock fall activities, such as impact craters, impacted trees, large blocks, etc. must be noted. A form, provided in the manual, can help the process.

http://www.ecorisq.org/


3.1.3 Preparation of input data

Input preparation requires the use of a GIS and the digital elevation model (DEM). Ortophotos, landcover maps, geological and vegetation type maps can also be very helpful in identifying homogeneous polygons within the study area. Data collected in the field strongly help in this phase. Ten raster maps need to be generated for the study area. In addition to the DEM, the user must create a map with the rock density of the source cell (i.e. area that can release a rock fall), 3 maps each containing one of the 3 diameters, one map containing the block shape of the potentially falling rocks, 3 maps describing the soil roughness, corresponding to the 70%, 20% 10% of the possible obstacle heights encountered by a falling rock, and finally a soil type map carrying values from 1 to 7, according to the manual. These 10 raster maps must have the same header.

If a simulation with forest needs to be carried out, two are the possible options. Option 1: 2 additional files need to be supplied, one carrying the East and North coordinate of each tree, together with its DBH, the latter carrying the percentage of conifer present in each polygon; option 2: supplying 4 raster maps, providing information on the number of tree per hectare, the mean DBH, its standard deviation and the percentage of conifers.

In case a simulation with protection barriers needs to be carried out, 3 additional maps are required. One map provides information on the cells belonging to the same structure (i.e. all cells belonging to the same barrier carries the same ID), the second map provides information on the barriers height, the third map on maximum energy that a barrier can bear.

3.1.4 Advantages/disadvantages

Advantages Disadvantages

Both detailed and regional application

Soil and forest characteristics can be described in detail

Trajectories are computed on a 3D-DEM

Results are in raster format and can be visualized into a GIS

A user friendly interface is provided for simulation parameters

Input preparation is time-consuming

Data collection in the field requires experience

Velocity results, only provided in terms of maximum value, overestimates real values

Scientific ability criteria

Realistic results x

Results comparable to field observations

x

Evaluation of forest protection role x

Possibility of back analysis x

Complexity of taking barriers into account

x

Usability

Complexity of input data process x

Applicability of default parameter x

Experience with the software required x

Preparation base data x

Output (which diagrams, graphics…) x


3.2 Rotomap

3.2.1 Description

The software Rotomap is a 3D software which is based on the DEM. It calculates a rock fall event with a mathematic model which can be described as quite “chaotic”: there is used a statistic way to determine the probable stopping zones of the blocks as well as the distribution of their kinetic energies. Even small variations of the input data cause huge variations of the end results.


During field investigations it is necessary to collect all relevant data, which are useful to calibrate the program.

First of all there has to be delimited the area of interest. Attention: The program is not able to calculate a “large” area with a small resolution. So be careful to select only the rock fall affected area and not a bigger one. Then there must be identified the source areas. Here you can also give a probability between 0 and 1,000 about how probable a rock fall event will occur. In this context it is also important to identify the block volume in m³. At the end also the soil parameters have to be identified: normal and tangential coefficient of restitution and the friction angle. A longer experience with the program can be very helpful in this context.


Figure 34: Data input window for Rotomap

All the data collected in the field have to be prepared as a raster files and then converted in a float (.flt). It is also quite simple to insert barriers, giving geographic coordinates.




Areal information

Rock fall barriers can be insert easily

The results can be visualized in a geographic information system in a raster format

It’s not possible to simulate “large” areas a regional scale analysis is not possible

If an error occurs, no detailed information about the real problem is given

Energies and bounce heights are quite overestimated


Realistic results x


x

Consideration of protection forest x



x

Usability

Complexity of input data process xx

Applicability of parameter default x

Experience with the software (long use) x




3.3 Georock 2D

3.3.1 Description

This program offers the user the possibility to simulate the rock fall process with two different methodologies: CPRS (Colorado Rockfall Simulation Program) and lumped mass. The first one was developed by Pfeifer and Bowen (1989). It is based on the modeling of spherical, rectangular and plate-type blocks on a free (vertical) fall. The calculated models derived from practical experiments. The lumped mass method however divides the slope in small lines with different dip angles and the falling block, simulated as dot-shaped spherical block, follows the rules of the gravitational force. Moreover it is possible to make a statistic and deterministic simulation.


To run Georock 2D relevant parameters such as the source zones, the block size etc., have to be known; they are helpful to calibrate the program. Impacts on trees are also relevant since the impact heights can be very helpful for the calibration of the bounce heights of the blocks. To define the slope characteristics the user can input his own data or use default parameters. Simulations run for the test-bed produced good results which have be very well correlated to the field observations.


Figure 35: Rockfall simulation window and results for Geprock 2D.

The first step consists in importing the topography into the program. Georock 2D uses x-z-coordinates to draw the profile and trajectories. Soil parameters were chosen among those proposed by the software. Attention must be paid as to the friction angle, which is not provided by the program. Additional parameters can be add by the user.




All single trajectories can be analyzed

CPRS and lumped mass simulation

Statistical and deterministic method

Simple to use

The export of the results is in RTF format (doc)

Outputs: energies, velocities, bounce heights, single trajectories…

Parameters default do not include friction angle


Realistic results x


x




x

Usability


Applicability of parameter default X





3.4 RocFall (Rockscience)

3.4.1 Description

RocFall is a 2D software, which is generally quite simple to use. The results are depending mostly from the soil parameters aside from the topography: normal and tangential coefficient of restitution and the friction angle. Apart from these parameters it is not possible to include the function of the protection forest in the simulation. Obviously the identification of the soil parameters requires an advanced knowledge of the geology and how parameters influence the software. The simple insertion of rock fall barriers is one of the best conveniences of the program. Generally the program works better when the slope is longer.

3.4.2 Field investigation

To use RocFall it is necessary to investigate all the relevant parameters, which can be helpful to calibrate the program in a right way. Obviously the general parameters like the source zones, the block size etc., have to be investigated. But it is also very important to investigate impacts on trees, because the impact heights can be very helpful in the calibration of the bounce heights of the blocks in the program. To define the slope characteristics there can also be used the parameters default. In our case they gave quite good results which could be correlated very well to the field observations. The software is very sensible to variations of the soil parameters, so that it is important to define them in a correct way. In this context a long experience in using the program could be helpful.


Figure 36: Rock fall simulation result with RocFall

The preparation of the input data for RocFall is quite simple. The profile can be imported into the software as a .dxf-file, which can easily be created from a geographic information system. Then the slope has to be divided according to slope/soil characteristics. New “materials” can also be added in the table. Unfortunately this program takes not into account the block form.




Simple to use

Parameters default are working very well

Bounce heights are often overestimated


Realistic results x


x




x

Usability







3.5 Rockfall (Dr. Spang)

3.5.1 Description

Rockfall is a 2D software which works a bit different than the other 2 described types. The program is able to simulate a maximum of 10,000 blocks. It calculates the trajectories according to the input data in a statistic way, so that it is advised to simulate at least 200 – 300 blocks. As an output the program gives a diagram about the distribution of the energy and the bounce heights.

3.5.2 Field investigation

As the program is a bit complicated, intensive field investigations are necessary. The exact trajectory has to be identified. But much more important is to find the right values for the soil parameters. In this case the program gives you a range in between the values can vary, but it does not give you an exact data. For this reason a long experience in using the software can be very helpful to improve the results.


Figure 37: Rockfall simulation result with Rockfall

Even Rockfall uses x-z-coordinates to insert the profile. But the coordinates have to be corrected in a special way (see handbook Rockfall). Then the exact values for the soil parameters have to be inserted by hand. It isn’t possible to save the data. The soil parameters don’t only consists of the tangential and normal coefficient of restitution but also include the rolling friction/rolling resistance and the amplitude and length of jumping block.




More possibilities to calibrate the results with detailed field investigations

Outputs can be printed/saved in .pdf-file

Limited possibility to zoom

Complicate to use


Realistic results x


x




x

Usability







4 A comparison between rockfall model performances

4.1 Application of Rockyfor3D at regional scale Hazard evaluations concerning rockfall within SS50-test-bed area have been carried out using different types of models and different methodologies. Primarily the scale of the analysis had been identified and a choice for an appropriate tool for a given scale was made. A new methodology for modeling rockfall over large areas was outlined for the SS50 road test-bed (Rolle pass road, PP4). This methodology consists of two subsequent scales of investigation. The hazardous process under investigation (rockfall) was investigated at both regional and local scale using the same modeling tool, Rockyfor3D [Dorren et al., 2009]. The goal of the regional scale analysis is providing an overview on the entire test-bed and spotting the most hazardous areas within the test-bed. To those spots a more accurate analysis is then applied. The main difference between the methodology applied at two different scales consists in the input data preparation. At regional scale input data are derived from thematic maps (vegetation species, geology, land use, faults, springs) and digital elevation model (DEM), whereas, at local scale, input data are collected in the fields. Geological data, ortophoto analyses and vegetation data allow to capture a general overview on the hazard level and to spot the most hazardous spots. These can be surveyed and studied in details. This procedure is, by far, more accurate than the previous, however it is time consuming, therefore its added value presents a counterpart of being expensive. Results of the regional scale methodology and application have been presented in Simoni et al. 2012.

4.2 Application of Rockyfor3D to some case studies To compare results of the modeling tool used for the regional scale analysis of rockfall along the SS50 road (PP4 test-bed) an additional study was carried out. The main objectives of the study is the comparison between the results obtained with a 3D software applied to specific (chosen) sections and those obtained with a 2D tool on those sections. The study takes into account 50 cases of rockfall occurred in Province of Trento during the last

3 years (2010-2012) (see Figure 38).

The tools used for the analysis are RockyFor3D (Dorren, L.K.A., 2012), for simulations of rockfall on a three-dimensional morphology, post-processed using 2D check profile-sections, and a two-dimensional model RocFall (Rockscience). Pair profile plots of results allowed to compare performances between the two models for any selected 2D sections.

The starting point used in the simulations is the DEM, derived from a LIDAR map with a mesh 1x1 m or 2 x 2 m depending on elevation (above or below 2000m). Other necessary data were collected through field investigations carried out with a GPS; information were collected about the morphological framework in which the rock fall occurred, about trajectories, impact locations, and arrest points.

Among all rockfalls occurred in Trentino in the last 3 years, these 50 case studies were chosen to capture different morphological and geological scenarios, different fracture systems, different dynamics and to asses strengths and weaknesses of modeling tools, in particular the appropriateness of each model for 2D or 3D analysis.

A sounded use of Rockyfpr3D requires data to be collected in the field, in particular data on soil type, block size, shape and volume, slope surface roughness, presence and type of vegetation, release points were collected. This phase is time consuming and sometime difficult due to demanding access to the site, however, if appropriately carried out, it greatly improves the model capability of reproducing rockfall dynamics and stopping distance. Differently from the regional analysis (see paragraph 4.1) in this study only data collected in the field were used as input parameters.

4.2.1 Input data (Rockyfor3D)

The procedure for input preparation was organized in three phases: Preliminary phase: Thematic maps have been elaborated to obtain vectors (shape files) whose associated table of attributes encompasses the information needed by the model which were to be collected in the field.

Data collection: Release points were identified in the field (also by climbing the slope where it was not too steep) as well as the trajectories and impacts were noted together with the type of soil and vegetation impacted. These have been sketched on a map together with the type of deposits and soils present along the slope.


Data processing: polygons were drawn to identify areas with homogeneous features, to cover the entire study area; for each polygon the relative entry in the attribute table was filled with the parameters collected in the field. At his stage ortophoto and thematic maps can also help in checking and completing field information. Subsequently vector files are rasterized to obtain input file for the model. Sometimes the preliminary data processing was used to calibrate the model, this phase was repeated several times by changing some parameters to obtain a faithful representation of the process detected in the field.

Figure 38: Locations of the 50 case studies for which a comparison between Rockyfor3D and Rocfall was carried out.

Advantages Drawback

Both soil parameters and blocks characteristics can be defined with great detail.

Input preparation is time consuming

Type and density of vegetation can be taken into account.

The evaluation of the parameters needed by the model requires experience and practice.

Trajectories are computed on a 3D topography, thus the run-out area is clearly marked.

If compared to real cases, the run out area is conservative

Results of the calculations are easily loadable in any GIS environment.

The model does not allow to change all the parameters for back analysis, i.e. some parameters are fixed (such as the normal restitution coefficients)

Results are reliable, provided input data are representative of the case study

Homogeneous soil type and deposits can be characterized by the same set of parameters

Table 8: Advantages and drawback of Rockyfor3D applied at local scale.


When a simulation is completed, a preliminary data analysis can be carried out by using the interface provided by the model, which allows visualize an envelope of simulated energies, passing heights along a 2D profile that has to be defined by two points.

Results are displayed in Figure 39 for one case selected out of the 50 (Fortini di Serravalle, Figure 38). Results have been validated in the fields. Figure 39 displays on the left the main input for the selected

case study, i.e. the surface roughness (Rg – see Rockyfor3D manual for details) and the soil type, which determines the restitution coefficients. Results, in terms of total kinetic energy [kJ] (top left) and passage height [m] (bottom left) are displayed on the right of the figure. These values are important for designing passive defense systems. Energies values allow to know the maximum energy absorbed by the defense systems, while the passage height allows to calculate the minimum height of the barrier.

Figure 39: Results from one example of one of the 50 case studies performed with Rockyfor3D. Top left: surface roughness and real events (yellow circles); top right: block energy [kJ], bottom left: soil type and events marked with circles, bottom right: passage heights (see Rokyfor3D manuals for details).

Figure 40 displays with a black line the terrain profile for the longitudinal section marked with a black dashed

line in Figure 39. Passage heights along this profile are represented with a pink line in Figure 40, while kinetic

energy is displayed in red as to 95% confidence interval and in green as to average values.


Figure 40: Terrain profile for the longitudinal section marked with a black dashed line in Figure 39. Passage heights along this profile are represented with a pink line. 95% confidence interval for kinetic energy is displayed in red, and n green as to average values.

4.3 Application of Rocfall to some case studies For details on the model see paragraph 3.4.

The 2D model Rocfall was run for the same profile Rockyfor3D was run (whose results are displayed in Figure 39 and Figure 40). The selected profile is visible as black dashed line in Figure 39. Results obtained for this

application mainly depend on soil parameters such as normal and tangential restitution coefficients and friction, which requires a good knowledge of quaternary deposits and geology in general. In addition to soil data, the model allows for passive defense structures (rock barriers) to be taken into accounts.

4.3.1 Input data (Rocfall)

Input data such as source area and block size were the same used for the 3D application. The model

parameters were calibrated with measures events (Figure 39); since it is sensitive to small variations of soil

parameters, it is important to define them carefully. In this context, a good experience with the program is valuable, since similar deposits can be given different coefficients to reproduce past events.

Preparation of input data. The profile visible as dashed line in Figure 39 was firstly extracted from the DEM,

then imported into the software as .dxf file. Parameters were assigned to reproduce the characteristics of the soil along the specific stretch.

Advantages Drawback

Easy to use Passing height is often overestimated for short trajectories (>20m)

The results of the processing are easily visible. Bounces are often overestimated if default parameters are used

Allows the user to easily change the parameters of the model to perform the back analysis

The presence of vegetation cannot be taken into account.

The shape of block cannot be taken into account.

Even deposits are homogeneous parameters can be different

The results of some processing, especially along short profiles are not very realistic

Table 9: Rocfall, advantages and drawback.


Results for the selected profile are displayed in Figure 41 in terms of trajectories and falling height. Comparing

results obtained with 2D simulations to results obtained with Rockyfor3D and surveyed past events, highlights that Rocfall provides reliable results for long trajectories (> 20m), on the contrary, for shorter trajectories,

results do not capture well enough past events. Table 9 summarizes advantage and drawback of this model.

Figure 41: Passing height along the selected profile (dashed line in Figure 39) computed using Rocfall.

Figure 42: Distribution of the total kinetic energy (rotational and translational) calculated along the trace of the section drawn in Figure 39


5 Field investigations

As mentioned in the chapters 3, field investigation are important for every simulation software, both 3D and 2D. Since the methodology is not changeable by the user, the only tuning action that can be performed by the user, in order to reconstruct a natural process (rock fall in this case), is providing an appropriate set of input data and parameters. Therefore field observations can help to calibrate results, impact heights on trees, soil

impacts. Figure 43 shows an example of field data input used to calibrate one of the 2D model.

Figure 43: Soil impacts of a rockfall event used to calibrate input data for 2D software (Atzwang/Campodazzo)

Testing the software mentioned in chapter 3 brought also to the conclusion that each program uses similar input data with small variations. While parameters like block shape, source areas, block volume are quite similar among all software, the soil parameters can also vary from software to software. Essentially, 3 well-known coefficients are generally used to describe the soil characteristics: normal and tangential coefficient of

restitution and the friction angle. Figure 44 illustrates 3 different soil behaviors, not deformable soil,

deformable soil and elastic soil. Additionally Rockfall (Dr. Spang) uses only the so called rolling friction/rolling resistance. This means, that the height of the obstacles along the trajectory can affect the process by dissipation of kinetic energy. Also RockyFor3D takes into account the surface roughness, this is described by 3

parameters which represent the probability that a falling block encounters a given size obstacle (Figure 45).


Regarding the block dimension most of the programs use the volume only. RockyFor3D instead, allows to define both the shape and the size of blocks by assigning 3 axes (d1, d2, d3). Therefore collecting data on the block volume and shape in the field is useful to set those parameters.

Figure 44: 3 different soil behaviors, not deformable soil (like bedrock, left), deformable soil (center) and elastic soil (right)

Figure 45: Representation of soil roughness implemented in Rockfor3D (Dorren, L.K.A. 2012)


6 Monitoring systems/early warning systems Monitoring is the process of checking, keeping track or measuring something, in a certain area, for a specific purpose and with defined criteria. Monitoring, together with field survey, is a fundamental observation methodology to analyses potential hazards and damages, and to collect ancillary data (Corsini, 2008). Monitoring are helpful to assess the spatial and temporal relationships between the phenomena of interest, their causal and triggering factors, and to define risk in quantitative terms (Fell & Hartford 1997). In case of natural phenomena, they are generally used to analysis the spatial, temporal, or kinematic characteristic of hazard, i. e. where, when and how severely the analyzed phenomenon can occur (Dai et al. 2002).

Monitoring systems are a complex combination of devices (sensors), data processing procedures and software, for hazard assessment purposes, more monitoring systems are generally coupled in integrated monitoring networks (Dunnicliff & Green 1993).

6.1 Monitoring systems for rockfall Monitoring systems in the context of rockfall sometimes represent a good solution to forecast upcoming rockfall events in case of changes of the stability of the rock face in question (Mölk, 2008). For the design of such monitoring systems several criteria needs to be decided upon in order to achieve an appropriate design: What is the failure mechanism – What are the parameters to be measured to safely estimate a failure? Where are the devices to be installed to monitor the relevant parameters and to detect a failure in time to react? Should the monitoring system serve as a warning system? What are the precision and tolerance necessary for the respective problem? Monitoring methods include:

Fissurometer/crack-meter: measures the opening change between selected cracks.

Geodetic survey/GPS: measures the change of the relative or absolute position of a block.

Interferometric radar: this method can be applied ground- or satellite based. It measures a change in the distance between the target and the radar device.

Laser scanning: measures changes in distances between the target and the laser scanner.

Extensometer: measures a longitudinal change along a measuring line

Tilt-meter: measures the inclination change of a given rocks


6.2 Control of existing protection measures (cadastres of protection measures) Protection measures and the knowledge of their status are of paramount importance in a context of monitoring and protection of a given target such as a road or a village. Organizing this information into a database is a means for managing maintenance and replacement. Within the context of PARAmount, the Province of Bolzano, in collaboration with the University of Bologna developed a new database for rockfall protection barriers. In the database the information relevant to the position, the geometry and the relevant dimensions, the principal components and corresponding materials, data on barrier certifications as well as technical or design reports were included. In particular, a capacity expressed in terms of kinetic energy absorption was associated to each catalogued item. It was especially useful to group the inventoried barriers in three principal categories: flexible, semi-flexible and rigid. Principal barrier types and subtypes were also identified.

Figure 46: A method of analyzing the response of falling rock protection barriers to produce reliable parameters for rockfall hazard assessment

Rockfall barriers are metallic structures typically made of three main parts: an interception structure, a

supporting structure and connecting components (Figure 47). The interception structure has the scope to bear

the block impact, the supporting structures keep the interception structure in place and the connecting components, which are the other system elements (e.g. longitudinal ropes, uphill cables, downhill cables and side cables, clamps, studs, energy dissipating devices) transfer the impact loads to all the foundations and anchorages.


Figure 47: Scheme of the main components of a typical falling rock protection barrier

6.2.1 Paramount database

In the database, the information relevant to the position, the geometry and the relevant dimensions, the principal components and corresponding materials, data on barrier certifications as well as technical or design reports are included. Information are also collected on the barrier state of maintenance. The database is then analyzed to identify the principal barrier types and subtypes and damage types and origin

Figure 48: Data collected on a semi-flexible barrier by in-situ surveys

The next step consisted in the development of a method to group all the inventoried barriers, including those for which the documentation was scarce or absent, under the identified and above mentioned types. Each barrier type features peculiar structural components and specific technical details. Therefore a specific interception structure, connecting component or supporting structure can be used to recognize a barrier type rather easily.


The data included in PARAMOUNT enable the identification of the type, origin and occurrence of the damages

which affect the Autonomous Province of Bolzano (PAB)’s falling rock protection barriers (Figure 49). The main

causes are:

design deficiency

wearing

events consequences

Figure 49: Top Types of flexible protection barriers within PAB. Bottom: types of semi-flexible and rigid protection barriers within the PAB

The most recurrent types of damage are:

structural elements failure;

corrosion;

accumulation of rocks boulders or fragments;

accumulation of mixed material

Fig. 25:


Figure 50: Types of flexible protection barriers within the PAB

Figure 51: Top: semi-flexible and rigid barriers and damage types. Bottom: flexible barriers and damage types


6.2.2 Prediction of the model failure and barrier capacity

For the semi‐flexible and rigid barriers, a procedure to predict the barrier actual capacity was developed. The procedure assumes that the cause of failure is the collapse of the structure itself, rather than the foundation failure. When structure collapse the model no longer converge, due to the formation of failure mechanism produce by the creation of plastic hinges. The value of kinetic energy corresponding to such circumstance is considered as an upper bound for the barrier capacity and the problem is traced back to the determination of the value of kinetic energy beyond which the model no longer converges. The procedure is as follows. By trial and error, a first non‐linear and dynamic analysis is carried out using a small impact energy value, chosen so that the model reaches the convergence. A new analysis is then carried out using an higher value of energy and the response of the model is observed. In general, plastic hinges may appear at this stage at the most stressed elements. If the model still converges, a further analysis is carried out by further increasing the intensity of the impact energy. The process is iterated until the barrier collapse. At this stage the procedure can be considered completed and the barrier capacity is taken as the impact kinetic energy in the second last analysis. Such energy level is the maximum energy level for the concerned barrier. For the flexible barriers, results of full‐scale test are generally available and the nominal capacity is known (Section 3). The experimental data are then used to develop numerical models of the barrier types which belong to this category. These numerical models are calibrated by reproducing the full‐scale experiments and then used to explore the barrier actual capacity, with special regard to the evaluation of the barrier capacity in presence of damages. In this case the actual barrier capacity is evaluated in relation to the nominal, following a procedure which will be presented and discussed in the next section.

1. Steel rigid barrier

Figure 52: A steel, rigid, barrier type in the Province of Bolzano

The numerical analyses were carried out following the procedure described. The FE model, made of three functional modules, was impacted, in the center, by a block shaped as a polyhedron. The model block was modeled using a set of non-structural concentrated mass as depicted in Figure 26. Velocity vectors of direction normal to the longitudinal beams were applied to the masses. The dimension of the block was chosen so that three longitudinal beams were involved in the impact. First analysis was carried out on the barrier model using a small energy value and plastic hinges appeared at this stage at the most stressed elements.


Figure 53: Formation of plastic hinges (in red) at the impact points

As this analysis converged further analyses were carried out, keeping the block mass constant and increasing the velocity applied to the lumped masses. Following this procedure the barrier capacity was obtained according to the procedure illustrated. The results relevant to the analysis which produced the barrier actual capacity (maximum energy level) are in

Table 10:

Block mass [kg] 311 Velocity [m/s] 1 Nominal Capacity [J] 100 Braking time [s] 0.015 Maximum elongation [m] 0.007 Maximum force at the foundation [kN] 30 Maximum moment at the foundation [kNm] 25

Table 10: Response of the barrier during an impact at the maximum energy level


2. The Tubre barrier type

Figure 54: The steel rigid barrier type Tubre

The FE model, made of three functional modules, was impacted, in the center, by a block shaped as a polyhedron. The model block was modeled using a set of non‐structural concentrated mass. Velocity vectors of direction normal to the longitudinal beams were applied to the masses. The dimensions of the block were chosen so that the central longitudinal beam and three longitudinal ropes were involved in the impact as depicted. The block dimensions were the same of the previously analyzed barrier model.

Figure 55: The three functional modules model of the barrier type Tubre



3. The Anas barrier type

Figure 56: The steel semi‐flexible barrier type Anas

The FE model, made of three functional modules, was impacted, in the center, by a block shaped as a polyhedron. The block was modeled using a set of non‐structural concentrated mass. Velocity vectors of direction normal to the longitudinal beams were applied to the masses. The dimension of the block were chosen so that three longitudinal ropes were involved in the impact as depicted in Figure 33. The block dimensions were the same of the previously analyzed barrier models. As this analysis converged further analyses were carried out increasing the impact energy. Following this procedure the barrier capacity was obtained. The results relevant to the analysis which produced the barrier

actual capacity are in Table 12.Table 11


Figure 57: Formation of plastic hinges (in red)



4. The Stecher barrier type

Figure 58: The steel semi‐flexible barrier type Stecher

The FE model, made of three functional modules, was impacted, in the center, by a block shaped as a polyhedron. The block was modeled using a set of non‐structural concentrated mass. Velocity vectors of direction normal to the longitudinal beams were applied to the masses. The dimension of the block were chosen so that three longitudinal ropes were involved in the impact as depicted in Figure 36. The block dimensions were the same of the previously analyzed barrier models. As this analysis converged further analyses were carried out, increasing the impact energy. Following this procedure the barrier capacity was obtained. The results relevant to the analysis which produced the barrier actual capacity are inserted in the figure and the table.

Figure 59: Deformed shape during the analysis



6.2.3 FE Models of flexible barrier type

The OM‐CTR 30A barrier type

The barrier model, made of three spans as illustrated in Figure 61, was subjected to a retrospective simulation

of the full‐scale test. As the barrier capacity is 3000 kJ the analysis were run at about 3000 kJ of kinetic energy. Impact tests on a barrier model were simulated using a three‐dimensional deformable body as test block, with mechanical properties equal to those of high resistance concrete. The block’s velocity was 25 m/s in the direction normal to the interception structure. The result are compared to those recorded in the experiments

in Table 14.

Figure 60: The flexible barrier type OM‐CTR 30A


Figure 61: The three functional modules of the barrier type OM-CTR 30A

Table 14: Experimental and numerical response of the barrier during an impact at the maximum energy level


The Safe 750 barrier type

Figure 62: The flexible barrier type OM-CTR 30A

The barrier model, made of three spans, was subjected to a retrospective simulation of the full‐scale test documented in the relevant Report. As the barrier capacity is 750 kJ the non‐linear and dynamic analysis were run at about 750 kJ of kinetic energy. Impact tests on the barrier model were simulated using a three‐dimensional deformable body as test block, with mechanical properties equal to those of high resistance concrete. The block’s velocity was 15 m/s in the horizontal direction and vy = 25 m/s in the vertical direction following the Report of the full‐scale tests carried out at an inclined test site.

Figure 63: The three functional modules model of the barrier type Safe 750


Table 15: Experimental and numerical response of the barrier during an impact at the maximum energy level


6.2.4 Numerical Models: toward the actual barrier

The nominal capacity can vary if the barrier has been installed in a configuration which diverges from the testing (experimental/numerical) configuration. This fact mostly apply to the rigid and semi‐flexible barrier types, whereas the flexible barriers are typically installed according to the manufacturer’s technical specification. The data contained in VISO have shown that the same barrier type (rigid and semi‐flexible) is often found in several geometrical configurations (e.g. post spacing, nominal height, presence of uphill or side cables). Furthermore, variation in the barrier elements have been also acknowledged (type and dimensions of beam’s section, cables diameters). According to the data, the effect of the variation of these parameters have been also investigated using numerical models. Although such procedure applies to any type of barrier, it is used in this section to study only selected barrier’s type.

Effects of the variation in the geometry and mechanical properties in the barrier capacity In this section numerical models are developed to investigate the effect of geometrical and mechanical properties with reference to selected barrier types. In particular the Anas and the Stecher barrier types are examined. These types of barrier are, in fact, very widespread on the PAB’s territory, in a variety of different configurations.

Geometrical and mechanical variation With regard to the Anas reference barrier type, the following geometrical variation are examined: posts spacing and nominal height. In both cases a three spans model of the modified barrier was subjected to the impact of block, modelled with a set of concentrated masses of velocity directed normal to the interception structure. Several analyses were run until the model no longer converged. Posts spacing. The following values

are considered: 3.5 m (a) and 6.5 m (b). In Figure 65 and Figure 66 the deformed shape of the modified Anas

barrier is shown for the maximum energy level analysis in case (a) and (b) respectively.

Figure 64: Deformed shape of the ANAS barrier type: a) 3.5 m post spacing


Figure 65: Deformed shape of the ANAS barrier type b) 6.5 m post spacing

Table 16: Response of the barriers during an impact at the maximum energy level

Table 17: Response of the barriers during an impact at the maximum energy level


Figure 66: Deformed shape of the ANAS barrier type: a) 2 m nominal height and b) 4 m nominal height

OBSERVATION ON THE EFFECTS OF THE DAMAGES In this section numerical models are developed to investigate the effect of damages on the barrier nominal capacity. The damages can be considered and included in the barrier model types to predict a possible actual capacity of the barrier. A method for the estimation of the reduction in the barrier capacity due to the presence of damages was developed. The method can be used for any barrier type, but has been devised with reference to a flexible barrier type. The procedure is as follows. The response of a barrier prototype, as observed during full‐scale tests carried out at the maximum energy level, is taken as the nominal or reference response. The reference response is described through the following quantities: nominal capacity, braking time, maximum elongation, residual height and forces mobilized at the anchorages and foundations. A FE model is then developed of the barrier prototype and the model parameters are calibrated by retrospectively simulating the full‐scale tests. The model should be simple but must ensure a good match with the experimentally observed response. The calibrated and verified model is then modified to include a possible damage. According to the examination of the damage types and origin illustrated in Section 3, the possible damages are: corrosion of structural elements, failure of structural elements, accumulation of rock or mixed materials. These damage types can be included in the FE models of a prototype as follows:

corrosion of structural elements: reduction of the element section

failure of structural element failure: removal of the element

barrier deformation after a concentrated or distributed impact: investigation of an impacted model (successive launches)

The FE model, suitably modified to accommodate the damage, can be subjected to a nonlinear and dynamic analysis to explore its actual response in terms of braking time, maximum elongation, residual height and forces mobilized at the anchorages and foundations. The analysis should follow the procedure used to obtain the nominal or reference quantities to enable a comparison of the model response in the two different conditions. At first, the analysis should be carried out at the maximum nominal energy level. If the results of the analysis ensure that:


forces and displacements mobilized within the damaged barrier do not exceed the nominal forces and displacements;

the barrier deformations are still acceptable;

the stresses mobilized within the barrier elements are kept within the admissible threshold

then the actual and nominal barrier capacity can be considered coincident. In case these requirements are not fulfilled, a further analysis should be carried out at a diminished energy level and the process ends when the results of the analysis ensure that all the requirements above stated are fulfilled. The energy level at which the last analysis is carried out can be then taken as the actual maximum energy level and actual barrier capacity.

The effects of structural element corrosion

The effect of the corrosion of the structural elements is investigated in this section with reference to two cases: a) The corrosion has produced a reduction in the section in all the cables equal to the 40%. As all the barrier cables feature a 20 mm diameter, the damaged model of barrier OM‐CTR 30A will present a 12 mm diameter cables. b) The corrosion has produced a reduction in the section of the cables within the interception structure equal to the 40%. As the equivalent truss elements of the model of barrier OM‐CTR 30A features an 8 mm diameter, the damaged barrier will feature an interception structure of 4.8 mm diameter cables.

The modified model of barrier OM‐CTR 30A (case a), made of three spans, was subjected to an impact at the maximum energy level. As the nominal barrier capacity is 3000 kJ the analysis were run at about 3000 kJ. Impact tests on a barrier model were simulated using a three‐dimensional deformable body as test block, with mechanical properties equal to those of high resistance concrete. The block’s velocity was 25 m/s in the direction normal to the interception structure. The impact at the maximum energy level produced a non-admissible deformation within a few elements in the longitudinal lower ropes, as depicted in Figure 48. Therefore, the block’s size was scaled down and further analyses were run up to a block size corresponding to

an energy level equal to 2000 kJ. As illustrated in Table 18, the response of the damaged barrier model

resulted comparable with the actual. The energy level at which this analysis was carried out is then taken as the actual maximum energy level and actual barrier capacity in presence of a significant corrosion of the barrier cable.

Figure 67: Deformed shape of the damaged barrier (corrosion, case a)) in the maximum nominal energy level (3000 kJ)

The modified model of barrier OM‐CTR 30A (case b), made of three spans, was subjected to an impact at the maximum energy level. As the nominal barrier capacity is 3000 kJ the analysis were run at about 3000 kJ.


Impact tests on a barrier model were simulated using a three‐dimensional deformable body as test block, with mechanical properties equal to those of high resistance concrete. The block’s velocity was 25 m/s in the direction normal to the interception structure. The impact at the maximum energy level produced a non-admissible stresses within a few elements in the interception structure. Therefore, the block’s size was scaled down and further analyses were run up to a value of size corresponding

to an energy level equal to 1500 kJ. As illustrated in Table 19, the response of the damaged barrier model is

now comparable with the actual. The energy level at which this analysis was carried out is then taken as the actual maximum energy level and actual barrier capacity in presence of a significant corrosion of the barrier cable.

Table 18: Experimental and numerical nominal and actual (corrosion, case a)) response of the barrier during an impact at the maximum energy level.

Table 19: Experimental and numerical nominal and actual (corrosion, case b)) response of the barrier during an impact at the maximum energy level

The effects of structural element failure The effect of the failure of a structural element is investigated in this section with reference to two particular cases: a) Failure of an uphill anchorage. The relevant uphill cable is removed. b) Failure of a side anchorage. The relevant side cable is removed. The modified model of barrier OM‐CTR 30A (case a), made of three spans, was subjected to a retrospective simulation of the maximum energy full‐scale test. As the nominal barrier capacity is 3000 kJ the analysis were run at about 3000 kJ. Impact tests on a barrier model were simulated using a three‐dimensional deformable body as test block, with mechanical properties equal to those of high resistance concrete. The block’s velocity was 25 m/s in the direction normal to the interception structure. The impact at the maximum energy level produced a value of maximum elongation higher than the reference and non-admissible deformations in some of the barrier elements.


Figure 68: Deformed shape of the damaged barrier (failure case a) in the maximum nominal energy level (3000 kJ)

Therefore, the block’s impact velocity was scaled down and further analyses were run up to a value of velocity corresponds to an energy level equal to 2500 kJ. The response of the damaged barrier model is now comparable with the actual (see table below). The energy level at which this analysis was carried out is then taken as the actual maximum energy level and actual barrier capacity in presence of a significant corrosion of the barrier cable. The modified model of barrier OM‐CTR 30A (case b), made of three spans, was subjected to a retrospective simulation of the maximum energy full‐scale test. As the nominal barrier capacity is 3000 kJ the analysis were run at about 3000 kJ. Impact tests on a barrier model were simulated using a three‐dimensional deformable body as test block, with mechanical properties equal to those of high resistance concrete. The block’s velocity was 25 m/s in the direction normal to the interception structure. The impact at the maximum energy level produced a peak force at the foundation of the external post higher than the reference. Therefore, the block’s impact velocity was scaled down and further analyses were run. However no value of kinetic energy were found which sufficiently lowered the peak force at the foundation.

Table 20: Experimental and numerical nominal and actual (failure, case a)) response of the barrier during an impact at the maximum energy level


The effects of a deformed barrier shape The effect of a deformed barrier shape is investigated in this section with reference to two particular cases: a) Barrier deformation caused by the impact of a single block of known kinetic energy (rock accumulation). b) Barrier deformation caused by the impact of a distribute mass of known impact velocity (mixed material accumulation). In order to investigate the case a) a set of numerical analyses was carried out on the OMCTR 30A barrier prototype. In particular, four identical and undamaged models of barrier OM‐CTR 30A (hereinafter called Model 1, 2, 3 and 4), each made of three spans, were subjected to central vertical impact tests. Model 1 was subjected to a test at the energy level of 100 kJ, Model 2, 500 kJ, Model 3, 1000 kJ and Model 4, 2000 kJ. At the end of each test the maximum elongation was measured and inserted in a graph which relates the impact energy and the elongation. Following this numerical testing program the deformed Models 1‐4 were subjected to a further impact to investigate the actual model capacity. For each of the models, the value of impact energy which produced a response close to the nominal and guaranteed the procedure requirements fulfillment, were recorded and

inserted in a graph which relates the first launch energy to second launch energy (Figure 68) providing an

estimate of the residual kinetic energy or actual capacity. As a result, the barrier deformation can be related to the impacting block energy , which can be in turn related to the actual barrier capacity using the graph below

(Figure 69and Figure 70).

Figure 69: Impact energy – maximum elongation relationship for an undamaged barrier OM‐CTR 30A (single block)


Figure 70: Impact energy – actual impact energy relationship for a damaged barrier (deformation case a) OM‐CTR 30A

(single block)


6.3 Monitoring systems : the experience of Forte Buso, SS50, Trentino, Italy A large rockslide is located at Forte Buso on the SS50 road, within the Paneveggio Natural Park, on the side of

the Paneveggio man-made lake (Figure 71). The area belongs to the eastern portion of the Southern Alps,

representing the sector of the convergent southern Alpine chain. This region is composed of two well-defined stratigraphic sequences: the "pre-Permian crystalline basement" and the "Permo-Cenozoic cover." The basement is intruded by plutons at different depths and basic acids. The "Permo-Cenozoic cover," consists of Paleozoic-Tertiary age sediments, partially folded in various stages of the Cretaceous to the Plio-Quaternary Sup.

Figure 71: Overview of the location of the rockslide.

6.3.1 Description of the area

The rockslide is located along the Passo Rolle road (SS50) between km 104 and 105. There is a man-made lake on the South side of the road. The road was originally located on the valley floor, then it was moved to the side when the artificial basin was filled. In the past this area used to be a quarry for porphyryc rock.

The rockslide area is located close to the summit of the slope and has a size of approximately 80x30x15 m . This area is formed by an accumulation of large blocks of porphyry. The peripheral portion is the steepest and experiences periodic detachments, characterized by varying size, ranging from debris blocks of few decimeters to large blocks of few hundred cubic meters.

The blocks are arranged according to the banks of the porphyryc rock, detensioning fractures are present on the surface and the movement is relatively slow. Velocity vectors have a radial pattern and move about 1cm per year. The sliding mass is morphologically not uniform, the top is nearly flat, while the body is rather steep. The volumes involved range from minimum values of 150-200 cubic meters to maximum 30-40.000 cubic


meters. Expected collapsing events have volumes ranging between 150 cubic meters and 1000 cubic meters. Fractures displays sizes ranging from 40-50 cm up to 3-5 m.

Figure 72: Technical map displaying the landslide above the main road

Figure 73: Top view of the rockslide, the lake is in the background


6.3.2 Sensors and monitoring

The first set of sensors was installed in September-October 1998; it consisted of strain gauges and inclinometers. Since a GPRS transmission was not available, data were collected manually. Afterword optical tracking, based on 30 optical targets installed on the rocks, were enabled. Records showed that all the blocks moved on average about 1 cm / year, while two boulders 4-5 m wide moved about 20-25 cm / 7-8 years. Additional metal wires were anchored, at one side, to the wall on the back of the sliding mass, and at the opposite side, to some of the block within the moving mass. When the cables stretch exceeded a fixed threshold, pre-warning and warning messages were issued to the person en charged. This system proved to be sometimes unreliable since issued false warning due to cable deformations induced by high temperature rather than by rock movements; this was the case especially in summer when the temperature can reach 36 degrees. In winter the weight of snow on the wires generated similar problems.

Later on the cables were connected to traffic lights and barriers to close the road, in addition 3 laser distometers and 2 wall 3-axial inclinometers were installed on the rockslide crown (not moving part) pointing at specific targets on the sliding mass. Better results were attained since the thermal expansion proved to be low. Continuous measurements, sampling every 15 minutes were taken.

The system has unfortunately proved to be inefficient due to the short time necessary for a block to reach the road (approximately 15 sec).

Recently optical monitoring with GPS total station was installed in addition to laser monitoring. A new protection wall was built at the toe of the rockslide, along the road with the aim of capturing the blocks before they reach the road. The wall covers almost the whole extent of the rockslide. The road facing part of the wall is reinforced by land armies (inclination 32 degrees) and cliffs (slope 65), the inside of the wall is covered with blocks standing on a 70 degrees slope. The wall height ranges from 6 to 7m and its width ranges between a minimum of 7-8 m and a maximum of 14 m. The excavation upstream has an inclination of 40°. During the wall construction workers were protected with rockfall barriers.

Currently the road is protected by rockfall barriers and the wall. During spring 2012 a series of large events occurred along the road which was closed for several days to remove the material and to stabilize the slide. After the event, the local government decided to fund a design of a new artificial tunnel to permanently solve the problems.

Figure 74: Vertical dispalcements measured at Forte Buso rockslide from 1998 to 2011


Figure 75: Displacement vector measured at Forte Buso rockslide

Figure 76: Map displaying the protection system at Forte Buso along the S.S. 50 road. Orange lines represent new rockfall barriers, the blue line is the wall and the purple lines represent ANAS rockfall barriers. Data taken from the cadastral


Figure 77: Detail on protection system (wall at the toe of the rockslide)

Figure 78: Design on the wall section


Figure 79: Detail on the protection system (ANAS rockfall barriers)

Figure 80: Detail on the rockfall events occurred in spring 2012 (before and after the events).


Figure 81: Lateral view of the rockslide. Some of the blocks have been marked to monitor their movements. Monitoring shows that a portion of the sliding mass is moving faster than the rest (red dashed line and arrow)


7 Bibliography Berger F. & Dorren L. (2006): Objective comparison of models using real size experimental data, Interpraevent 2006, 8 pp. BUWAL (1998) - Methoden zur Analyse und Bewertung von Naturgefahren. Umweltmaterialien nr. 85 Christopher P. Russell, Dr. Paul Santi, Dr. Jerry D. Higgins (2008) – Modification and statistical analysis of the Colorado rockfall hazard rating system. Report No. CDOT-2008-7. Final Report, 124 pp. Corsini A. 2008, Monitoring Methods: systems behind a safer environment, pg 47 -54, results of the international conference Monitor. Dorren, L.K.A., 2012. Rockyfor3D (v5.0) revealed – Transparent description of the complete 3D rockfall model. ecoriQ paper (www.ecorisq.org): 31 p. Mölk, M., 2008, Rock fall monitoring, Monitoring methods: systems behind a safer environment, pg. 99 – 101, results of the international conference Monitor Orlandini, S., Moretti, G., Franchini, M., Aldighieri, B., Testa, B., (2003) – Path-based method for the determination of nondispersive directions in grid-based digital elevation models, Water Resourc. Res. 39, 6. Piacentini D. & Soldati M. (2008): Application of empiric models for the analysis of rock-fall run-out at a regionale scale in mountain areas: Examples from the Dolomites and the Northern Apennines (Italy), 9 pp.

Piacentini D. & Strada C. (2006) – Linee Guida – Convenzione per l’analisi e la perimetrazione delle aree di possibile propagazione crolli. Autonomous Province of Bolzano. Internal report.

Provincia Autonoma di Bolzano – Linee Guida alla redazione di piani del pericolo (Regolamento di esecuzione alla legge urbanistica provinciale” Decreto del Presidente della Giunta Provinciale ,23 febbraio 1998) REGIONE LOMBARDIA. (2011) – Linee di indirizzo per le progettazione delle opere di difesa del suolo in Regione Lombardia. Regione Lombardia territorio e ubanistica, 165 pp. Simoni, S., Pasquazzo, R., Zampedri, G., Campana, R., Cocco, S., 2012, A new methodology for a 3D rockfall hazard analysis at regional scale - application to a case study along the Grappa and Rolle pass road. INTERPREAVENT Grenoble.

http://www.ecorisq.org/


Acknowledgements

The authors would like to express their sincere gratitude to all stakeholders and observers, who actively participated in PARAmount and provided valuable input data, information and feedback throughout the whole project.

These include

- Ripartizione Servizio Strade, Autonomous Province of Bolzano

- Servizio Gestione Strade, Autonomous Province of Trento - DICAM Department, University of Bologna - DISTART Department, University of Bologna - Trentino Trasporti

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