A Study on the Evaluation of Cost Variation Caused by Geotechnical Risk Involved in Underground

Construction Projects

Hiroyasu Ohtsu Department of Urban and Environmental Engineering, Kyoto University, Kyoto, Japan

E-mail: ohtsu@geotech.kuciv.kyoto-u.ac.jp

ABSTRACT: This paper presents the design methodology considering the evaluation of geotechnical risk involved in underground construction projects. In details, a basic method to evaluate the variance of construction cost due to geotechnical risk adopting geo-statistics theory is presented. Finally, as concluding remarks, results point out the applicability of basic concept presented in this study for discussions on unforeseeable geological conditions.

1 INTRODUCTION

Currently, the severe economic condition in Japan has come to foster national concerns on the promotion of infrastructures. As a methodology to satisfy this national demand, the necessity to investigate cost-benefit of construction projects has been highlighted. Up to now, many sophisticated models to evaluate benefits of construction projects have been proposed. However, researches on the reasonableness of construction cost have not sufficiently been studied. The reason for it is that construction projects essentially involve many uncertainties and/or risks (Flanagan and Norman, 1993; Chapman and Ward, 1997). Among various risk factors involved in the execution of construction projects, geotechnical risk, which is so-called unforeseeable geological condition, is a typical one (Ohtsu et al., 2002A).

It is well known that underground construction projects involve uncertain factors related to geological conditions, which are very difficult to completely foresee in design phase. The difficulties concerning unforeseeable geological conditions, actual construction of underground structures such as caverns and/or tunnels generally often require the modification of support patterns, which are prescribed in accordance with rock classification pre-determined based on preliminary in-situ survey. Consequently, the modification often causes cost overrun, which damages not only interest of project owner but also social benefit.

Until now, in research fields of project management, the problem associated with who bears extra-expense due to cost overrun has been always main theme to be discussed. However, until now, discussions associated with the evaluation of geotechnical risk have not been made sufficiently in Japan. The reasons for it would be guessed as follows: 1) Owners with deep pockets represented by government bodies had been able to allocate

budget enough to cope with unforeseeable geological condition during execution of

construction projects by re-measuring the amount of items involved in construction fees. 2) Contractors also had not paid serious attention to the

financial loss due to the delay of the completion of projects, since their concern on projects were limited to the total

amount of contracted cost paid by owners. In other words, geotechnical risk has been

generally regarded as an act of God. Consequently, it was very rare to discuss the maturity of basic design involving uncertain geotechnical factors.

However, the adoption of new procurement system of infrastructures, private finance initiative, PFI project based on Engineering-Procurement-Construction, EPC, contract, may

Table 1 Examples of PFI Hydropower Producing Projects in The world

Project name Country )MW (Layout Contract Status Casecnan Philippin

e 150 Underground power

house EPC Construction

('00) San Roque Philippin

e 345 Dam (200m) +

Tunnel EPC Finance closed

('99) Theun Hinboun

RPD Lao 210 Tunnel Non EPC Operation ('98)

Houay Ho RPD Lao 150 Dam (77m) + Tunnel Non EPC Operation ('00) Khimti I Nepal 60 Underground power

house Non EPC Construction

('00) Birecik Turkey 672 Dam (62m) EPC Construction

('00) Ita Brasil 1,450 Dam (125m) EPC Construction

('00) Guilman-Amorin

Brasil 140 Dam (41m) + Tunnel EPC Operation ('98)

Figure 1 An Example Composition of Players Part in a PFI project

change current risk allocation rule of geotechnical risk between owners and contractors drastically, as discussed later. As shown in Table 1, many underground construction projects have been executing under the EPC contract even in developing countries.

Here, the serious problem due to the adoption of the new procurement system is that the system requires transparent explanation on risks involved in projects for participants in projects, whose majors are not engineering. Figure 1 schematically shows the fundamental composition of a PFI project jointed by various types of players as follows: ← Shareholders ← Project Company; Single Purpose Company ← Engineering Procurement Construction Contractor ← Various subcontractors concerning the construction and/or the equipment ← Lenders

Table 2 Risk Allocation Rules of Geotechnical Risk by Standard Contract

Contract name Contract Condition Risk Allocation

Owners Contractors

GCW Design and/or Construction Separation

○

FIDIC Red Design and/or Construction Separation

○

FIDIC Yellow Design and Construction ○ FIDIC Silver EPC/Turn Key ○

GCW: General Conditions of Government Contract Works of Building and Civil Engineering Construction (1989)

The most important step stone to execute a PFI project is to get the approval of all participants, whose majors are not engineering, such as shareholders, lenders and so on shown in Figure 1. In other words, without the agreement with response to all risks including geotechnical risk between each participant, the PFI project is never started. Therefore, the documentation of clear understandings among the participants is inevitable.

Authors have proposed the basic concept to express project cost fluctuation due to

geotechnical risk by adapting the definition of risk established in financial engineering filed (Ohtsu et al., 2002B). In financial engineering, based on probabilistic theory, return and risk are defined as expected value and standard deviation respectively. In a same manner, by considering the variance of geotechnical parameters involved in design, which is modeled adopting geo-statistics theory, standard deviation of construction cost can be regarded as risk measured due to geotechnical risk.

From such a viewpoint, this paper presents the methodology to evaluate geotechnical risk

involved in underground construction projects based on financial engineering theory. In details, the applicability of risk-expected plane proposed in this study is presented and a basic methodology to evaluate cost fluctuation due to geotechnical risk adopting geo-statistics theory is also presented. Finally, as concluding remarks, results obtained in this study point out the applicability of the concept presented in this study for discussions on unforeseeable geological conditions.

2 ALLOCATION RULE OF GEOTECHNICAL RISK IN CONSTRUCTION CONTRACTS

In order to reconfirm the treatment of geotechnical risk specified in various contract article, this study pick up four construction contracts, GWC (1989), FIDIC Red (1999A), FIDIC Yellow (1999B) and FIDIC Silver (1999C). In Table 2, both the contract condition and the rule of risk response to geological risk corresponding to each contract are summarized. As shown in Table 1, the other contracts except FIDIC Silver are based on re-measurement contract, and FIDIC Silver is based on turnkey contract. Therefore, the treatment of geotechnical risk specified in various contract articles listed above could be interpreted as follows:

← Under contract conditions except FIDIC Silver, an owner essentially pays the expense relating to geotechnical risk as shown in Table 2. ← Under the contract condition of FIDIC Silver, EPC contractor essentially pays the

expense relating to geotechnical risk as shown in Table 1. Based on the findings mentioned above, it seems that without the adoption of FIDIC Silver, contractors never suffer financial losses from geotechnical risk. However, it should be noted that though financial losses of contractors is reduced by re-measuring the amount of items involved in construction fees based on Bills of Quantities, BOQ, contractors may suffer indirect loss due to the delay of the completion of projects caused by geotechnical risk. Therefore, not only owners but also contractors should pay serious attention to the investigation of losses caused by geotechnical risk in spite of contract conditions. Furthermore, it is not also to mention that in a PFI project, EPC contractors should pay the most serious attention to the investigation of geotechnical risk. In order to reconfirm the treatment of geotechnical risk specified in various contract article, this study pick up four construction contracts, GWC, FIDIC Red, FIDIC Yellow and FIDIC Silver. In Table 2, both the contract condition and the rule of risk response to geological risk corresponding to each contract are summarized. As shown in Table 2, the other contracts except

Figure 2 The Schematic View of the Variance of the Construction Cost

FIDIC Silver are based on re-measurement contract, and FIDIC Silver is based on turnkey contract. Therefore, the treatment of geotechnical risk specified in various contract articles listed above could be interpreted as follows: • Under contract conditions except FIDIC Silver, an owner essentially

pays the expense relating to geotechnical risk as shown in

Table 2. Under the contract condition of

•

Figure 3 The Plot of The Distributionof Construction

FIDIC Silver, EPC

contractor Cost on Risk-Expected Plane essentially pays the expense relating to geotechnical risk as shown in Table 2. Based on the findings mentioned above, it seems that

without the adoption of FIDIC Silver, contractors never suffer financial losses from geotechnical risk. However, it should be noted that though financial losses of contractors is reduced by re-measuring the amount of items involved in construction fees based on Bills of Quantities, BOQ, contractors may suffer indirect loss due to the delay of the completion of projects caused by geotechnical risk. Therefore, not only owners but also contractors should pay serious attention to the investigation of losses caused by geotechnical risk in spite of contract conditions. Furthermore, in a PFI project, EPC contractors should pay the most serious attention to the investigation of geotechnical risk. 3 BASIC METHODOLOGY

This chapter presents the basic methodology with associated with how to estimate the variance of construction cost as geotechnical risk.

3.1 General View

A project owner provides geological condition of the project based on preliminary investigation to contractors as fundamental information for bidding. However, it is easily understood that a lot of uncertain factors would be involved in geological condition presented by owners. If one permits the variance involved in geological condition, one should consider the methodology associated with how to evaluate the variance in design phase. A solution to the problem mentioned above is the adoption of probabilistic modeling of geotechnical properties. Based on the results of design considering the variance involved in geological condition numerically, the adoption of probabilistic modeling would enable to estimate the variance of the construction cost required to the completion of the investigated project as shown in Figure 2 (a). In Figure 2, standard deviation σF, which is regarded as so-called risk in finance engineering field, is presented as the index to denote the variance of the construction cost. The expression helps participants in projects, whose major is not civil engineering, understand the variance of the construction cost caused by geotechnical risk. Here, necessity of the additional in-situ investigation as a method to investigate geotechnical risk should be stressed. It is easily understood that the additional in-situ investigation following preliminary research would be very effective to investigate the variance involved in geological condition. However, the limitation of the budget allocated for site-investigation makes it difficult to make decision as to whether the additional in-situ investigation is necessary or not. In order to overcome the difficulty mentioned

above, the expression in accordance with the risk definition of financial engineering, as shown in Figure 3 helps to decide effectively. For an example, if the standard deviation σA, which denotes the variance of the construction cost calculated based on the results of the additional in-situ investigation as shown in Figure 2 (b), is obtained, the benefit of the execution of the additional in-situ investigation, B, would be expressed as follow:

σF

≥σA

(1) B = σF − σA

It is obviously impossible to evaluate the standard deviation σA explicitly prior to the execution of the additional investigation. Therefore, the standard deviation σA presented here would be regarded as an imaginary value. However, the value would be very useful to discuss the relationship between the cost of the additional in-situ investigation and the benefit to be obtained corresponding to the investigation in the earlier stage. And, after the investigation, it is possible to directly discuss the relationship between the invested cost and the benefit accepted actually. From such viewpoints, this study adopts Kriging method, which is a method of geo-statistic theory, to evaluate the variance involved in geological condition. 3.2 Basic Concept of Kriging Method Applied in This Investigation

In Kriging method, which is a method called Best Linear Unbiased Estimator, BLUE, it is assumed that optimum estimated value Z0* would be expressed as the following linear function:

Zo * =∑λ0 iZi (2) i

In which, Zi and λ0 i denote the measured values and the corresponding weights at measured point i respectively. In this study, as for the method to define the spatial distribution of estimated values, semi-variogram, which associates the value, Z(x), at the location of x with the value, Z (x +h ), at the location of x+h, is adopted as follow:

γ()h = 1 E{[Z(x +h)−Z()x ]2} (3)

2

In which, E{x} is the operator to denote expected value corresponding to value x. In general, various types of functions are proposed as to the function defining semi-variogra() and also various methods are proposed as to the methodology to optimize the

γh approximate function. In this study, in order to simplify the methodology, semi-variogram γ()h is modeled as the spherical function and non-linear least square method is adopted. By solving the following equations, both λ0 i and µ are finally obtained:

ji

∑λ0 γ(xi −xj )+µ=λ0 γ(xi −x0 ) j

(4)

∑λ0 i =1 i

The Borehole The Borehole outside Embankment inside Embankment

Embankment

River Alluvium Sand Layer

Alluvium Clay Layer

Diluvium Layer

Figure 4 Geological Profile of Sub-layers under the River Embankment Substituting the obtained λ0 i into equation (2), optimum estimated value Z0* is obtained. Furthermore, estimated error variant, σE2, is also evaluated as follow:

σ E 2 = µ +∑λ0 iγ(xi − x0 ) i

(5) 3.3 Case Study

This study examines the geological profile of river embankment shown in Figure 4 as an example to investigate the variance involved in geological condition. On this site, feasibility study, F/S, associated with highway construction project beside river embankment is being planned. Since the case study to determine the formation level of highway to be constructed is being carried out at the current stage, geometrical information such as the width of sub-layers under the river embankment is one of the most dominant factors affecting the estimation of construction cost. Therefore, in this study, as the first step of F/S, the spatial distribution of

Frequency

40

30

20

10

0

-6-4-20 2 4 6 Depth　(O.P. m)

Figure 5 Histogram of the Depth of Diluvium Layer

geometrical information such as the Figure 6 Semi-Variogram of the Depth of Diluvium Layer width of sub-layers is focused on. As shown in Figure 5, based on the results of in-situ investigation using boreholes, which are approximately placed with the interval of 200 m in the longitudinal direction, the sub-layer in this site are classified to three layers, alluvial clay layer, alluvial sand and diluvium layer.

4 RESULTS AND CONSIDERATION

In this study, Kriging method is applied to the evaluation of the spatial distribution of the width of each sub-layer. Firstly, Figure 4 shows histogram of the depth of diluvium layer, which are obtained with the averaged interval of 200 m. It is not necessary to explain that it is a very important factor to determine the formation level of highway to be constructed. As shown in Figure 5, both the mean value and variant of sampled data are 0.00 and 1.40 respectively. Consequently, the variation of sampled data is relatively small. Secondly, among the results of obtained semi-variogram, which are substituted into equation (2), Figure 6 shows the semi-variogram of the depth of diluvium layer. As shown in Figure 4, the spherical function obtained shows the good coincidence with measured values.

Finally, the results of the spatial distribution of the depth of diluvium layer evaluated by

Depth(O.P.m)

Figure 7 The Comparison of the Results of Kriging

Kriging method are discussed. Figure 7 (a) shows the spatial distribution of the depth of diluvium along the center axis of the river embankment in the left side of the river. The result plots both the estimated value and the estimated value ± standard deviation respectively. Since the variant of sampled data, which is essentially equivalent to the sill of semi-variogram, is relatively small, the values estimated by means of Kriging seems to have very high preciseness. In order to make the findings related to the preciseness of the estimated values obvious, Figure 7 (b) shows the spatial distribution of the depth of diluvium based on the sampled data, of which interval is 800 m. The results clearly point out that the estimation on geological condition using sampled data with wider interval of boreholes gives less preciseness than that with smaller interval of boreholes

Here, as an example to discuss the effect of preciseness of in-situ investigation on the design of actual project, by using results shown in Figure 7, the variation of the construction cost due to the change of interval of boreholes is examined. In details, the sensitivity of the thickness of alluvial soil layers shown in Figure 7 on construction cost of retaining wall excavation works with steel sheet is investigated.

Table 3 Construction Cost Variation of Sheet Pile

Estimated value

Estimated value + Standard

deviation

Estimated value -Standard deviation

200m Interval

173,780m2 176,480 m2 171,175 m2

295,426,000 yen

300,016,000 yen 290,997,500 yen

800m Interval

173,740 m2 180,150 m2 167,245 m2

295,358,000 yen

306,255,000 yen 284,316,500 yen

Figure 8 The Plot of Construction Cost on Risk-Expected Value Plane

Table 3 shows cost variation of retaining wall excavation works with steel sheet, which are estimated based on the depth of alluvial soil layer of the estimated value and the estimated value ± standard deviation respectively. Furthermore, Figure 8 shows the plots of estimated cost on risk-expected value plane in accordance with Figure 3.As shown in Figure 3, using sampled data with smaller interval of boreholes obviously can reduce risk on construction cost. Consequently, the finding mentioned above shows that the relationship presented in equation (1) holds. However, the difference between both cases on expected value of construction cost is very little. When expected value of construction cost does not vary, the effectiveness of additional in-situ investigation such as borehole survey should be discussed by comparing the benefit B presented in equation (1) and the cost due to the execution of additional in-situ investigation. In this example, the benefit B is worthy of 6 million yen as shown in Figure 8. However, the cost of in-situ investigation with the small interval of boreholes is much more expense than the benefit mentioned above. In other words, this result reveals that though additional in-situ investigation is very effective to reduce risk involved in construction works, the expected benefit is less than extra-expense associated with the execution of additional in-situ investigation. From such a viewpoint, results shown in Figure 8 obviously point out that the effect of geometrical condition on construction cost in this example is relatively small. Consequently, this methodology of risk evaluation associated with geotechnical risk helps to investigate the cost-benefit of in-situ geotechnical survey.

However, the discussion presented in this paper is limited to the sensitivity of geometrical factors on construction cost. Therefore, further investigation considering the effect of mechanical properties on construction cost should be required, in order to verify the applicability of risk evaluation method in this

paper.CONCLUDING REMARKS

In this study, by applying geo-statistics theory to geotechnical properties observed at boreholes around river embankment, the features of the spatial variation of geotechnical properties observed are discussed. Finally, results shown in Figure 8 obviously point out that the effect of geometrical condition on construction cost in this example is relatively small. Consequently, it is pointed out that this methodology of risk evaluation associated with geotechnical risk helps to investigate the cost-benefit of in-situ geotechnical survey.

However, the discussion presented in this paper is limited to the sensitivity of geometrical factors on construction cost. Therefore, additional investigation considering the effect of mechanical properties on construction cost should be required, in order to verify the applicability of risk evaluation method in this paper.

References:

Central Construction Industry Council of Japan, 1989, Standard Form of Agreement and General Conditions of Government Contract for Works of Building and Civil Engineering Construction, Eighth Edition (In Japanese).

FIDIC, 1999A. Conditions of Contracts for Construction for Building and Engineering Works Designed by the Employer, First Edition. FIDIC, 1999B. Conditions of Contract for Plant and Design-Build for Electrical and Mechanical Plant and for

Building and Engineering Works by the Contractor, First Edition. FIDIC, 1999C. Conditions of Contract for EPC Turnkey Projects, First Edition. Flanagan, R. and G. Norman 1993. Risk Management and Construction, Blackwell Science. Chapman, C. and S. Ward 1997. Project Risk Management, John Wiley & Sons. Ohtsu, H. and Y. Ohnishi, 2002A. A Study on The Risk Management Methodology of The Oversea Construction

Projects from A Viewpoint of Contractors, Journal of JSCE, No.707/VI-55, pp.207-218 (in Japanese). Ohtsu, et al., 2002B. A Study on The Evaluation of Geotechnical Risk Based on Geo-statistic Theory，Proc. of Probabilistics in GeoTechnics: Technical and Economic Risk Estimation, Graz, Austria, pp.113-120.