extended abstract - ulisboa
TRANSCRIPT
Management Models of the Degradation of Buildings – Influence of Degradation Factors in the Appearance of Stains
on Facades
Extended Abstract
Jorge Miguel Macieira da Costa
Supervisor: Prof. Dr. Pedro Vaz Paulo (IST) Co-Supervisor: Prof.Dr. Fernando António Baptista Branco (IST)
Lisbon, December 2011
Extended abstract
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1. Introduction
The present dissertation follows the line of research on methodologies to the service life prediction of
components and constructive materials developed by Paulo [2009], based on the inspection of
buildings. The elements analyzed were the building's facades, and the anomaly studied was the stains
caused by the retention of dirt collection.
The main objective of this research is to evaluate the influence of degradation factors in the
appearance of stains on the facades using degradation models, and their ability to provide tools to
estimate the service life of paintings on facades, considering different deterioration factors.
2. Methodologies of service life prediction
2.1. Service life prediction theories
In the last two decades, the methodologies to the prediction of service life of components and
constructive materials were driven by the investigations of various entities.
The CIB1 organization has created some commissions where there has been developments and
investigation in the following areas:
• “Performance Concept in Building” (W60) – published a series of reports based on the
concept of building’s performance.
• “Maintenance management and modernization of buildings facilities” (W70) – the commission
has the objective of promoting a deeper understanding of the influence of the built
environment on human behavior, health and organizational productivity, the promotion of a
strategic and operational value of the buildings’ management and make connections between
financial institutions, technical, sociological and operational aspects of the buildings’
management.
• “Prediction of Service Life of Building materials and Components” (W80) – the commission
addresses the prediction of the useful life of components and constructive materials through
the identification and development of systematic methodologies and areas of improvement of
the existing methodologies, recommending new methodologies and reporting on the state of
the art.
• “Building Pathology” (W86) – the commission is essentially based on the learning of the
construction pathologies and on the encouragement of the systematic application of this
knowledge to the design, construction and building management. Its goal is to produce
information that will assist in the effective management of buildings, develop and evaluate
methods for assessing anomalies and propose methods for prevention and mitigation of
construction defects.
1CIB – International Council for Research and Innovation in Building and Construction;
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Through the research of the organization RILEM2 results a recommendation, published in 1989,
“Systematic methodology for service life prediction of building materials and components” for the
prediction of the service life of the components and constructive materials. This recommendation was
the basis for the development of standards for the service life prediction - ISO3.
In the United Kingdom it was published in 1992 the standard 7543 “British guide to durability of
building element, products and components” which states various methods for the prediction of the
service life of components and constructive materials. According to the standard mentioned above, the
service life prediction can be accomplished through:
• The experience obtained, with the same or similar constructions, submitted to similar weather conditions.
• The evaluation of the level of degradation of the elements exposed in a short duration, by estimating a limit value for the durability.
• Accelerated aging tests.
The Architectural Institute of Japan [AIJ, 1993] proposed a methodology to predict the service life of
the constructions that allowed a great development in this area that later was translated, resulting in
the “Principal Guide of Service Life Prediction Buildings“, distinguished by the innovation of the use of
the factorial method.
2.2. General procedure of service life prediction
The standard ISO 15686-2 [2001] exposes various methods for the prediction of the service life of
materials and components, exposed to several conditions of exposure. Paulo [2009] distinguishes
three fundamental phases: problem definition, data collection and data analysis, being this
methodology adopted in the present work.
In the problem definition it is established the scope of the study to develop. At this stage it is defined
what are the materials analyzed, its characteristics, its application context, the possible degradation
factors that will influence the service life of the materials or components, which tests will be adopted in
the stage of data collection, and finally the type of data analysis to use and the desired output.
Subsequently to the definition of the problem, it is carried out the data collection to obtain information
about the mechanisms of degradation and the anomalies of the material, as well as the identification
of the degradation factors that influence the evolution of these mechanisms.
After the data collection comes the creation of degradation models and resulting estimates of the
service life of the material or component.
2 RILEM - International Union of Laboratories and Experts in Construction Materials, System and Structures;
3 ISO – International Organization for Standardization;
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3. Methodology
3.1. Problem definition
The present work was carried out using the service life prediction methodology of the paintings over
the stains of dirt collection, evaluating the influence of the degradation factors: "paint surface texture",
"color", "prominent element - balcony" and "solar orientation" in the appearance of the stains.
To obtain the data, the methodology was based on the inspection of 131 buildings in service, and on
the grouping of data according to the degradation factors adopted. After the data collection, this was
analyzed using degradation graphics, through the modeling of Gompertz, Potential and Weibull
curves.
3.2. Data collection
3.2.1. Building inspections
The inspection of 131 buildings located in the area of Madragoa, Lisbon, aimed at the visualization
and registration of buildings with different levels of dirt collection in the facade.
The inspection was based on photos and their fitting picture of the facade, the determination by visual
inspection of the paint surface texture, color, the existence of element - salient balcony, and solar
orientation. Finally, the obtaining of information on the date of application of the paintings in the
buildings, in the municipal Archive of Lisbon.
The photographic record was executed through an informatic application, which consisted in the
elaboration of the image of the facade through several partial photos of the facade and
orthogonalization of the resulting image. The described process is presented in Figure 1.
Figure 1 – Photo montage of the building.
3.2.2. Paint surface texture determination
The procedure adopted for the identification of the texture of the film consisted on a visual inspection,
which allowed a clear distinction between three types of texture. Figures 2,3, and 4, present the
textures considered.
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Figure 2 - Example of a “textured”
paint surface texture
Figure 3 - Example of a “smooth –
plastic” surface texture
Figure 4 - Example of a “smooth -
oil”. surface texture
3.2.3. Color identification
The determination of the facades colors was based on the classification adopted by TPBR4. Table 1
presents the classification adopted.
Table 1 – Color of the outer surface of the sun protection [TPBR]
Coat color Light Medium Dark
Solar absorption coefficient 0,4
0,5
0,8
Color
White
Cream
Yellow
Orange
Light red
Dark red
Light green
Light blue
Brown
Dark green
Bright blue
Dark blue
Black
In Table 2 it is presented an example of the classification adopted.
Table 2 – Example of color classification
Light Medium Dark
JC118 JC128 JC029
4 Thermal Performance Building Regulation
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3.2.4. Identification of prominent element
Through the visual evaluation it was established a classification of two levels: building with balcony
and building without balcony, to make the identification of the prominent elements. Below is presented
an example of the adopted classification (Figure 5 and 6).
Figure 5 – Building with balcony (JC103)
Figure 6 – Building without balcony (JC081)
3.2.5. Determination of solar orientation
To determine the solar orientation of the facade it was used a watch from brand casio, which indicated
the degrees and consequently the cardinal point (North, South, East and West). Figure 7 presents a
scheme with the classification adopted.
Figure 7 – Classification Scheme of solar orientation [adapted from Garrido, 2010]
3.2.6. Dirt collection quantification
Through the PhotoColor application of the platform BuildingsLife, it was possible to quantify the dirt
collection on the buildings facades. Paulo [2009] states that the PhotoColor application was developed
to characterize the color and its variations on a facade without human observation errors, in order to
quantify the defects based on the analysis of the color of the facade.
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3.3. Data analysis
3.3.1. Degradation models
After having completed the data collection from the field work, and the Photo Color application, it was
used an overall degradation graphic of type T-ED (time - Extension of degradation), which formed the
basis for obtaining the various graphics of degradation in terms of degradation factors adopted, where
the abscissa represents the age of the painting since the last maintenance and the ordinate the
retention of dirt collection.
3.3.2. Degradation curves
In order to model the deferred performance of the paintings against the stains, in the present work,
were used the modeling curves like Gompertz, Potential and Weibull, whose equations are given
below (Equation 1, 2 and 3):
�� = ����� (1)
� = �� (2)
� = 1 − ������
�
(3)
In the equations mentioned above, �� ,�e � represent the extent of dirt collection, expressed as
percentage, in total area of the facade, t indicates the time passed since the last painting done and the
parameters , �, � e β are used in the fitting of the curves to the data in the degradation graphs.
To make the adjustment of the curves of degradation it was necessary to use a mean square error
minimization process (MSE). This process consists primarily of calculating the errors of the abscissa
and ordinates axis. The error measured in the abscissa axis, is an error concerning the age difference
identified by ����, being these the real age of the painting of the building and the age the model
obtained by quantification relatively to the existing dirt collection. In the ordinate axis, the measured
error is related to the difference between the quantification of real dirt on the facade and the
quantification of dirt obtained by the model identified by ����, depending on the age of the painting of
the building.
In order to optimize the degradation curves considering the errors defined in the abscissa axis and in
the ordinate axis, it was created an indicator called Combined Mean Squared Error (CMSE), whose
acquisition is presented in the following Equation 4.
���� = ���� + ��� × 10# (4)
Note: The multiplication performed to the value of ��� aims to put this value in the same order of magnitude of
����
Extended abstract
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4. Discussion of results
4.1. General degradation graph
Gompertz type degradation curves, Potential and Weibull were fitted to the graph of overall
degradation, as shown in Figure 8, being possible to observe the equations of the curves in Table 3.
At first glance, in Figure 8, it is possible to observe a dispersion of the points on the general
degradation graph. This situation, according to Paulo [2009] is expected and is considered as being
an important indicator of the quality of data, since there are several factors that exert a significant
influence on the durability and performance of materials.
Figure 8 – Degradation curves of the overall sample
Table 3 – Equations of the degradation curves
Gompertz Potencial Weibull
�� = ��$,%&#'�(),)*+,� �- = 4,13460��&�',&1231 � = 1 − ��� �
#3,3'%'$�4
4.2. Influence of the degradation factors
4.2.1. Paint surface texture influence
The degradation curves obtained for the degradation factor "paint surface texture," are shown in
Figure 9. In Table 4 are the respective equations.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70
Dir
t co
lle
ctio
n e
xte
nt
(%)
Coating Age (years)
Sample
Gompertz
Potencial
Weibull
Extended abstract
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Figure 9 – Degradation curves obtained by application of the “paint surface texture” degradation factor (Gompertz curves).
By analyzing the degradation curves obtained, it can be noted a delayed worse performance of
textured paints comparing with other paints. The dispersion of data, reflects the influence of other
additional degradation factors that were not considered in the presented analysis.
Table 4 – Equations for the degradation curves obtained by application of the “paint surface texture” degradation factor
Gompertz Potencial Weibull
Textured �� = ��$,&5151�(),)6,47� �- = 3,96187��&�',&%;#3 � = 1 − ��� �
'$,12%'�4
Smooth - plastic �� = ��$,1&$5%�(),)*,)4� �- = 6,44357��1�#,;2523 � = 1 − ��� �
##,&1%&5�4
Smooth - oil �� = ��3&,$1'';�(),)=4++� �- = 4,62851��$�#,&&;'% � = 1 − ��� �
5$,%12;5�4
4.2.2. Influence of the color
The classification of the degradation factor "color" was based on Table V.5 of RCCTE (see Table 1),
considering three levels of color, light medium and dark. The implementation of this filter resulted the
degradation curves shown in Figure 10, whose equations are presented in Table 5.
Figure 10 – Degradation curves obtained by application of the “color” degradation factor (Gompertz curves).
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70
Dir
t co
lle
ctio
n e
xte
nt
(%)
Coating age (years)
Textured
Smooth - plastic
Smooth - oil
G-T
G-SP
G-SO
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70
Dir
t co
lle
ctio
n e
xte
nt
(%)
Coating age (years)
Light
Medium
Dark
G-Light
G-Medium
G-Dark
Extended abstract
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By the observation of the graph, it can be concluded that the dark paintings have a better performance
due to the similarity of tones with dirt.
For light and medium colors, they present a worse performance, which is predictable because the light
paint color in coating easily reveals the existence of stains.
The similarity of behavior between the light and medium colour paintings is emphasized, being
possible in another alternative to adopt a classification in two levels.
Table 5 – Equation for the degradation curves obtained by application of the “color” degradation factor
Gompertz Potencial Weibull
Light �� = ��1,#'&$2�(),)*)76� �- = 1,52406��&�',23#23 � = 1 − ��� �
'2,51'#1�4
Medium �� = ��3#,#&3$3�(),?)+*)� �- = 3,06630��1�#,';$51 � = 1 − ��� �
#3,3%55'�4
Dark �� = ��'3,#%3#%�(),)6664� �- = 4,62851��$�#,$355& � = 1 − ��� �
5;,255%&�4
4.2.3. Influence of prominent elements
With the application of the degradation factor “salient element - balcony”, were obtained the
degradation curves shown in Figure 11, being possible to visualize the respective equation in Table 6.
Figure 11 – Degradation curves obtained by application of the “salient element – balcony” degradation factor (Gompertz curves)
In the analysis of the resulting degradation curves of the graph, it is clear that the best performance
over time is of the facades without balconies. That was indeed expectable since there are no salient
elements on the facade, which provides the natural washing effect through water on the total extent of
the facade. Regarding resulting degradation curve for facades with balconies, it was also expected a
worse performance than the degradation curves without balconies, so the existence of prominent
elements prevent the effect of washing in the total length of the facade contributing to the appearance
of particles deposits because they prevent the access of water.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70
Dir
t co
lle
ctio
n e
xte
nt
(%)
Coating age (years)
With balcony
Without balcony
G-With balcony
G-Without balcony
Extended abstract
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Table 6 – Equations for the degradation curves obtained by the application of the “prominent element – balcony” degradation factor.
Gompertz Potencial Weibull
With balcony �� = ��%,5&52$�(),)6+=+� �- = 1,56788��&�',21;#; � = 1 − ��� �
'2,%;23$�4
Without balcony �� = ��3#,#&3$3�(),?)+*)� �- = 5,47601��1�',%2'2; � = 1 − ��� �
#2,$15#$�4
4.2.4. Influence of solar orientation
The degradation curves resulting from the application of the degradation factor "Solar orientation" are
shown in Figure 12, and the respective equation in Table 7.
Figure 12 – Degradation curves obtained by application of the “solar orientation” degradation factor. (Gompertz curves).
With the analysis of the graph, as expected, the north oriented facades are favorable for the retention
of dirt, because of the low solar radiation and consequently the greater humidity accumulation. The
facades west, east and south oriented have a similar behavior, with a better performance against dirt
retention
Table 7 – Equations for the degradation curves obtained by application of the “solar orientation” degradation factor.
Gompertz Potencial Weibull
North �� = ��&&,#5&'$�(),?*4=)� �- = 4,60883��&�',&$$;% � = 1 − ��� �
'$,&$'$&�4
South �� = ��3',&1%&%�(),)666+� �- = 5,47413��1�#,3;'&3 � = 1 − ��� �
#','3'23�4
West �� = ��';,'1#1$�(),??+4=� �- = 4,62851��$�#,$2##2 � = 1 − ��� �
#3,'&;1&�4
East �� = ��1,2$2'$�(),)*@44� �- = 4,62851��$�#,&%2&5 � = 1 − ��� �
#3,2%$;3�4
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70
Dir
t co
lle
ctio
n e
xte
nt
(%)
Coating age (years)
North
South
West
East
G-N
G-S
G-W
G-E
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4.3. Combination of degradation factors
After performing the analysis of the influence of each one of the degradation factors applied
separately, it is presented in this section the combination of the degradation factors with the aim of
finding more detailed degradation models.
The degradation factor "paint surface texture" was adopted in the three combinations, as observed
earlier, a factor that reflects a major influence on the evolution of the degradation of paintings. It was
also studied the possibility of making the combination of the three factors but its application would
produce a big fragmentation of data taking into account the collection of buildings.
Following, it is presented an example of the results obtained, from the combination of the “paint
surface texture – textured” and the “solar orientation” factor, the last factor is grouped into two levels.
In level 1 we have the facades oriented to the North and in the level 2, the facades oriented in South,
West and East. The obtained degradation graphs and curves are presented in Figure 13, with the
respective equations presented in table 8.
Figure 13 – Degradation curves obtained by application of the “paint surface texture – textured” and “solar orientation”
degradation factors (Gompertz curves)
Table 8 – Equations for the degradation curves obtained by application of the “paint surface texture – textured” and “solar orientation” degradation factors.
Gompertz Potencial Weibull
Tex
ture
d Level 1 �� = ��1,3&#;1�(),??6,?� �- = 9,47877��&�',&$'$$ � = 1 − ��� �
3%,';$&2�4
Level 2 �� = ��$,2#%$5�(),)6?+6� �- = 4,79600��1�#,;%$2$ � = 1 − ��� �
'2,$3&21�4
The analysis of the degradation curves shows a significant difference in the performance of textured
paintings according to its solar orientation, highlighting a better performance level 2 of the paintings
that are oriented at South, West and East.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70
Dir
t co
lle
ctio
n e
xte
nt
(%)
Coating age (years)
Level 1 (North)
Level 2
(South;West; East)
G-Level 1 (North)
G-Level 2
(South;West;East)
Extended abstract
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5. Conclusions
It was assessed the influence of the degradation factors in the appearance of stains on facades,
demonstrating the ability to make estimates of future needs for a particular painting because of dirt
collection, knowing its texture, color, the existence of prominent elements or its solar orientation,
because these curves were obtained to represent the effects of these four degradation factors. The
application of the degradation factors can be performed independently or simultaneously in order to
observe the performance of the paintings over time.
In order to identify other degradation factors that influence the appearance of stains, it is suggested
the adoption of other degradation factors such as the influence of precipitation, the wind and the
pollutants gases or even the conditions and methodology used in the application of coating
It is considered that it is important to conduct further inspections of paintings already made by other
authors as Paulo [2009] and Garrido [2010] in order to monitor the evolution of the anomalies and
make comparative studies.
The improvement of adopted methodology also includes the combination of more degradation factors,
so one of the limitations in this study consisted on the combination of only two degradation factors, an
imposed limitation given the number of the samples. To improve this aspect, it is advisable to increase
the buildings samples. As seen, the combination of factors proved to be of great importance in order to
achieve curves more appropriate to the characteristics of each type of paint and environmental
exposure, enhancing the importance of the considerations made previously.
References
Architectural Institute of Japan. (1993). The English Edition of Principal Guide for Service Life
Planning of Building. Japan : Architectural Institute of Japan.
Garrido, M. (2010). Service Life Prediction of Facade Paint coatings in Traditional Buildings .
Master thesis in Civil Engineering, Instituto Superior Técnico.
ISO 15686-1:2000. (2000). Buildings and constructed assets: service life plan ning - part1:
general principles . International Standard Organisation, Geneva, Switzerland.
ISO 15686-2:2001. (2001). Buildings and constructed assets - Service life pla nning - Part 2:
Service life prediction procedures . International Standard Organisation, Geneva, Switzerland.
Paulo, P. V. (2009). A Bulding Management System (BuildingsLife): Applic ation of deterministic
and stochastic models with genetic algorithms to bu ilding façades . Doctoral thesis in Civil
Engineering: Instituto Superior Técnico.