ENVELOPE INDEX EVALUATION MODEL OF EXISTING
BUILDINGS
M. Fernanda S. Rodrigues
Aveiro University, Civil Engineering Department
Campus Universitário de Santiago – Aveiro
Portugal
José M. Cardoso Teixeira
Minho University, Civil Engineering Department
Campus de Azurém – Guimarães
Portugal
J. Claudino P. Cardoso
Aveiro University, Civil Engineering Department
Campus Universitário de Santiago – Aveiro
Portugal
António J. Batel Anjos
Aveiro University, Matematics Department
Campus de Santiago – Aveiro
Portugal
ABSTRACT
An evaluation methodology to estimate social housing envelope degradation level was
developed which has been applied to a set of social housing. The degradation level of each of
the principal anomalies was determined as well as the evaluation index of the building
envelope. Degradation evaluation results were obtained through visual survey and were
aggregated by a method develop for the research. With the aim of discovering the subjacent
mechanism of the visual phenomena, models have been constructed to analyse the behaviour
of each dependent variable (anomalies), in function of the independent ones: building age,
cover type, last repair action, proximity to sea, trees, main roads, industrial zones and
collective buildings. After obtaining the building index evaluation, models were developed
that permit the estimation of the influence measure of the predictor variables in this index.
The aim of this paper is to present the models for obtaining the envelope index evaluation of
a set of dwellings. This has been used in the scope of a research project on prioritising
refurbishment interventions in the Portuguese social housing stock (Rodrigues, 2008).
KEYWORDS: Evaluation index; Model; Visual survey methodology; Existing buildings; Social
housing; Envelope anomalies; Degradation level.
INTRODUCTION
The degradation of buildings’ envelope is one of the main concerns of owners and is frequently the
root cause of rehabilitation actions that can improve their external appearance (Balaras et. al. 2005).
Indeed buildings` image is closely related to the quality and durability of its envelope, the decay of
which causes negative evaluation and rejection by users and the public as a whole. The quality of
design and construction is essential to prevent early decay and ageing of the buildings’ external
envelope. Therefore the buildings’ envelope must ensure high durability and resistance to external
environmental agents and enable high standards of internal comfort for users (thermal, acoustical, air
quality, lighting, etc.). Because of the substantial investments that are continually being made in social
dwellings, it is essential to carefully pick the best possible design and construction options for the
external envelope of these buildings. Adequate solutions must balance costs, performance and
quality so that they may be economical and long lasting therefore leading to the lowest possible total
costs, measured in terms of initial costs and maintenance costs throughout the project life. This
applies both to new buildings and to existing buildings needing rehabilitation of their external envelope
(LNEC, 2004).
In the 1970’s the Japanese government, owner of a huge stock of social homes, started facing the
problems of maintaining and rehabilitating buildings evidencing early degradation symptoms. Several
studies were developed on this topic and a rehabilitation guide was published in 1989, translated into
English in 1993 1 . The guide indicates the factors to consider for selecting the best possible
rehabilitation options for increasing buildings’ durability. Relevant steps of the proposed methodology
obviously include measuring the building’s performance and its level of degradation. Beyond the
Japanese studies, several other similar initiatives have been developed elsewhere (Hovde, 2004)
therefore reflecting the importance of this topic.
In Portugal, the results of the 2001 Census show that there is a substantial number of housing
buildings needing repair and rehabilitation (INE, 2001-a). Moreover, the census displays qualitative
and quantitative data on the level of conservation or degradation of all buildings surveyed but it is not
specific for social housing units (INE, 2001–a and 2001-b). However, evaluating buildings’
performance and degradation level as well as predicting their future evolution is essential for deciding
the repair and rehabilitation requirements (INE 2001-a). This article presents a methodology for the
visual survey of the main anomalies of the external envelope of buildings. The methodology was
applied to a set of dwellings in Portugal2 and this allowed identification of the main visible anomalies
on their external envelope. In order to achieve this, two evaluation scales have been built: one for the
degree of degradation applicable to each typified anomaly and the other for the performance level of
each building in respect of a set of functional requirements. The degradation level (DL) of each
anomaly was established and results for all typified anomalies were subsequently aggregated yielding
the evaluation index (EI) of the external envelope of each building surveyed. Using the values of EI,
models were developed for estimating the influence level of the predictors’ variables (anomalies) on
the index.
1
Principal Guide for Service Life Planning of Buildings (AIJ, 1993)
2
Buildings surveyed were erected under the low cost regime (cost controlled system that allows for public co-financing), in
the Aveiro district after 1971 and are rented (in the public renting system that comprises social benefits to tenants) and are
currently managed by the local municipality.
BUILDING ANOMALIES
Building degradation
According to Harris (2001), the decay noticed in buildings is a natural process and unavoidably takes
place in time, not being necessarily the result of design error or construction deficiency. In fact the
mechanisms of deterioration are the consequence of the interaction of two independent variables: the
building, as a physical object and the environment, as a source of agents. The building starts
decaying immediately after construction, starting with materials in an invisible way. This is the
incipient stage – deterioration takes place but with no visible damages. The second stage is the fast
deterioration – mechanisms started before aggregate and become visible. Shortly afterwards, the
building components start failing, culminating with the total building failure and eventual
abandonment. Although the degradation of building components is a normal consequence of the
ageing process, there is a set of factors influencing that process, such as building quality, weather
conditions, lack of maintenance, and so on. These factors will increase the building operation costs
and expand the rehabilitation needs, if no actions are taken to halt the degradation process. Actions
include maintenance, repair and rehabilitation that must be applied to the building elements. The
duration of the built elements depends not only of their physical, chemical and mechanical properties
but also of the maintenance conditions and environmental exposure they are subjected to (Sarja et
al., 2005). In order to establish the building degradation level two sets of factors must be taken into
consideration – the durability conditions of the building and the degradation factors acting upon it –
both of which contribute to trigger the degradation process (AIJ, 1993).
Main building anomalies
Several methodologies have been developed for evaluating anomalies of the building envelope. In
order to get statistical data on the quality of buildings at national level, the Recommendations (2000)
stress the importance of including the variable “conservation state” in national censuses. In Portugal,
this was implemented in the Census of 2001 within the “building questionnaire” through the “repair
needs” record (INE, 2001–a and 2001-b), but causes for anomalies detected were not in the scope of
the Census. In order to assess the repair needs, a specific scale was previously developed (INE,
2003), but it also does not consider background causes for the repairs required. Another method was
developed in Portugal for evaluating the conservation level of dwellings and setting up rent update
factors (MAEC, 2006) but it is not supported by a sound assessment system and it does not consider
causes for anomalies either.
Accordingly, data on causes of building anomalies is scarce in this country but the literature survey on
this topic has shown identical conclusions extracted from several sources, so it seems acceptable to
consider it representative of the Portuguese reality as well Henriques (2001). In his work, Chamosa
(1984) collected the information available in several European countries and concluded that the
Spanish data is close to the average European data on this issue. The analysis of this survey shows
that the main source of anomalies is design errors, followed by construction problems and material
deficiencies in the third place. These results basically agree with the general conclusion one can take
by randomly analysing building construction in Portugal, although contradicting the general conviction
that the main causes lay in the construction process. However, in March 2006 the Agence Qualité
Construction, published a report on construction quality in France from 1995 to 2005 and concluded
that a lower number of anomalies were caused by design, assigning about 80% of anomalies
detected in buildings to construction causes (AQC, 2006).
The incidence of anomalies on the external envelope of buildings appears to have a great importance.
According to several sources of data (BRE, 1988; Trotmant, 1994; CQF, 1997; HAPM, 1997; Watt,
1999; INE, 2001-a; AQC, 2006), about 50% of anomalies recorded negatively affect the external
building envelope. Moreover, according to a survey of BRE (1988), these anomalies directly
contribute to the decrease of about 50% in the performance of important building functional
requirements (waterproofing, durability and maintenance) and substantially influence others (thermal
insulation and acoustic performance) in over 10% of related requirements. The work of BRE further
identified a set of anomalies of the external building envelope: water penetration, condensation,
humidity, cracking, detachment, noise transmission and visual deterioration, among others.
The effect of anomalies in buildings’ envelope is the decrease of performance of their functional
requirements and the increase of investment needed in corrective actions for repair and rehabilitation.
Therefore the prediction of emerging anomalies and building performance evolution are key factors
for establishing maintenance and rehabilitation strategies of the housing stock. Indeed, the large
weight of maintenance costs of facades when compared to the total maintenance costs of buildings
evidences the need for sound prediction of the former for decreasing the latter (Teo and Harikrishna,
2006).
The extent of damage is another important issue for which some type of measurement scale is
required. Several qualitative scales have been developed and diffused, such as Socotec’s (2002).
Other scales for graduating building envelope anomalies have also been found in the literature
(Gaspar and Brito, 2005; Gaspar et al. 2006; Lounis et al., 1998; Marteinsson and Jonsson, 1999;
Teo, 2005; Shohet and Paciuk, 2006). Based on this survey, the research project supporting this
article developed an evaluation scale, specific to the reality surveyed as described below.
Finally, a number of evaluation methods for measuring the building performance and subsequently
supporting the decision of corrective measures have also been found in the literature, basically falling
under the following classes (Sarja et al., 2005):

Multi-Attribute Decision Aid (MADA)

Quality Function Deployment (QFD) method

Risk Analysis.
METHODOLOGY
Overview
The evaluation of the degradation level of the external envelope of buildings surveyed was based on
visual survey and interviews with a group of tenants, with the asset managers from local authorities
and with the person in charged of the building’s current management3.
Using visual survey for building anomaly assessment has been extensively reported in the literature
with special emphasis on the EPIQR methodology that combines visual survey with inquiries to
tenants and visits to their apartments (EPIQR, 2004). Similarly, the survey performed in this project
comprised of visual survey of the external building envelopes and visits to at least three apartments in
each building, preferably located in different facades and on different floor levels (one on the ground
floor or first floor, another on the top floor and the last on an intermediate floor). Common areas of
each building have also been looked at (Bluyssen, 2000; EPIQR, 2004)
The visual survey was adequate for the dimension of the sample under analysis (Balaras et al., 2004,
citet by Gaspar and Brito 2005) and revealed to be a straightforward, easy to use and low cost
approach. The aims of the visual survey were to identify anomalies, assess their level of severity,
gauge their root causes and suggest correcting measures. The severity level was assessed through
evaluation scales the deterioration parameters of which were previously defined. Evaluation methods
used in buildings generally point to the assessment of each building element taken separately
typically through five degradation or performance levels [Gaspar and Brito, 2005].
Interviews comprised of a visit to each tenant’s home to check anomalies previously reported by
them, assigning a sound graduation level to those anomalies and complementing the external survey.
Moreover, interviews also helped in assessing the quality of the internal environment of houses by
enabling the detection of thermal and acoustic pitfalls.
The approach followed in the diagnosis was previously prepared and normalised for the whole set of
buildings surveyed, enabling a reliable evaluation of their external envelope, setting up adequate
rehabilitation actions and establishing corrective measures for the improvement of energy efficiency
and internal air quality.
Evaluation Scale
To support the visual survey of the external building envelope, tables applying the Failure Mode and
Effects Analysis (FMEA), were used to analyse the principal causes and effects of the identified
anomalies. The aim of this analysis is to identify the degradation types that can affect the envelope as
the deterioration chains develop (Table 1).
Element
3
Function
Table 1 – Observation table structure - FMEA
Failure Mode
Causes
Direct Effects
This is typically one of the tenants elected by the others for that purpose over a year or two.
Indirect Effects
This method permits the obtention of the relation between the deterioration state of the elements in
analysis and their performance level. It has particularly been applied to obtain the service life cycle
and degradation models of construction systems and products. In each case the failure mode
represents the degradation process (Lair and Chevalier, 2002). It is based on an interactive principle:
the direct or indirect effects can be the causes of the degradations, giving the possibility of identifying
almost all the possible failure modes. The FMEA will be complete when all the possible degradation
chains that lead to the components’ and products failure have been established (when they cannot
perform one of their principal functions) (CIB W080, 2006).
The visual survey supported by FMEA was measured through a qualitative and quantitative
evaluation scale. The aim was to quantify the identified external anomalies by their level of severity by
means of an evaluation scale. The scale makes use of deterioration parameters for each level, by
associating a visual scale with a physical scale, similar to the scale used by Shohet and Paciuk
(2006). The assessment takes into account the intensity, extension and location of damages
detected.
Table 2 shows the eight level evaluation scale used in the survey. This was set up on the basis of the
Hermione scale (Sarja et al., 2005; ALBATROS, 2005), the lowest level of which (R), was condensed
to two levels with the following meanings: R+ for unacceptable degradation cases but where
rehabilitation is still possible through exceptional rehabilitation actions, and Rº for very severe
situations.
Table 2 – Evaluation level
Description/Action
G+
Gº
GY+
Yº
YR+
Rº
Degradation
Level (DL)
Exceptional without any intervention required. Plan maintenance actions to
10
safeguard the conservation level
Good without reservation. Regular cleaning and maintenance actions needed
9
Good with some minor reservations. Cleaning and maintenance actions needed
8
for the elements evidencing deterioration symptoms.
Acceptable but needing small rehabilitation actions.
7
Acceptable but needing moderate rehabilitation actions.
6
Acceptable but needing large rehabilitation actions.
5
Unacceptable. Priority intervention. Major rehabilitation.
4
Unacceptable. Unsuitable for rehabilitation. Demolish/substitute.
3
Aggregation of results
During this research it was decided to assess the following functional requirements:
- waterproofing of the envelope (roof, external walls and frameworks);
- external visual aspect (covering cracks, detachment, spread of vegetable and micro organisms,
broken glazing, degradation of roof and rain water drainage system);
- durability.
Results from the visual evaluation were aggregated into a global value for each element and/or
anomaly surveyed. The aggregation approach adopted for that purpose was first based on the
Hermione qualitative method (ALBATROS, 2005) but it was later concluded that it should be modified
into a quantitative method, for which eight graduation levels were adopted. Accordingly, a method
was constructed that allows the obtaining of a global degradation level for each element and/or
anomaly surveyed by aggregating the graduations assigned to each area or element examined on the
external envelope of buildings. Moreover, departing from these results, the Evaluation Index (EI) was
consigned to each building’s envelope.
MODEL FOR OBTAINING THE EVALUATION INDEX
Multiple-linear regression model: anomalies – degradation factors
Multiple-linear regression models encompass a set of statistical techniques used for modelling the
functional relations among variables and for predicting the value of one or several dependent
variables (answer variables) in function of a set of independent (or predictors) variables (Maroco,
2003). The aim of the model adopted in this project was to analyse the behaviour of each anomaly
surveyed (dependent variables) as a function of a set of independent variables considered: building
age, type of covering, last maintenance action or repair and nearby environment (sea, trees, building
areas, main roads with heavy traffic, industrial areas, and so on). This allowed the estimation of the
underlying mechanisms that had probably contributed to the level of degradation detected by visual
inspection of the external envelope of the buildings surveyed. By using this approach, Teo (2005)
modelled the defects of facade painted coatings and Shoehet and Paciuk (2006), studied the
mechanisms of failure (or degradation influencing factors) of several facade coverings. These models
permit the establishment of the source and the extension of the building envelope’s anomalies. The
outcomes of the model for data collected in this research project are summarized in Table 3. Some
remarks follow:

For discoloured covering of the external facade, the following set of independent variables
was used: building age, type of covering, last maintenance action or repair, closeness of trees
areas and proximity of residential areas. No explanatory model could be found for the
variation of the dependent variable with the degradation influencing factors. The same applies
to the detachment of facade coverings (related to the type of covering, the building age and
the last maintenance action or repair) and to moisture spots (related to the type of covering,
the building age and the last maintenance action or repair and the closeness of the coast,
forested areas and dense building areas).
Table 3 – Anomaly Severity Variation Model: Coefficients and Statistical Values of the Linear
Regression Performed
Anomaly
Variable
(Constant)
last maintenance
/repair action
Building age
Cracks
Unstandardized
regression
Coefficients
5,607
Standard
Error
Stand.
Coeff.
0,297
T
Value
Sig.
R2
R2aj
18,908
0,000
0,214
0,182
0,274
0,260
0,361
0,336
0,192
0,064
0,378
2,983
0,04
-0,246
0,095
0,329
-2,591
0,013
7,591
0,300
5,325
0,000
-1,720
0,392
-0,524
-4,389
0,000
8,906
-0,849
0,137
0,168
-0,581
65,028
-5,053
0,000
0,000
-0,588
0,231
-0,292
-2,541
0,014
Discoloured
covering
Covering
detachment
Spread of
vegetable and
micro organisms
Superficial
calcium
carbonate
deposit
(efflorescence)
(Constant)
Trees proximity
(Constant)
Trees proximity
Seaside proximity
Humidity
Sig. – Significance;
R2aj – R Square adjusted;
R2 – R Square – variance proportion explained by the model;
R – Correlation coefficient;
Predictors in the model: Constant.

For cracks on facades, only two independent variables were considered from the initial set,
the model having a Pearson adjustment coefficient of R= 0.462 and R2= 0.214. Moreover, the
model shows that 18.2% of the average variation of the degradation level related to cracks on
facades which may be explained by the age and by the last maintenance action or repair,
while the remaining 81.8% of the average variation may be explained by other factors not
considered in this study.

For dark spots on facades (mainly due to micro organisms culture and dirt remains), only
one variable was considered from the initial set, the model having a Pearson adjustment
coefficient of R= 0.524 and R2= 0,214. Moreover, the model shows that 26.0% of the average
variation of the degradation level related to this deficiency may be explained by the proximity
of forest areas, while the remaining 74.0% of the average variation may be explained by other
factors not considered in this study.

For efflorescence spots, only two independent variables were considered from the initial set,
the model having a Pearson adjustment coefficient of R= 0.601 and R2=0.361. Moreover, the
model shows that 33.6% of the average variation of the degradation level related to this
deficiency may be explained by the proximity of forest areas and costal areas, while the
remaining 66.4% average variation may be explained by other factors not considered in this
study.
Although some correlation was found between independent and dependent variables in all cases
studied, results show low average variation of each dependent variable relative to the corresponding
predictors variables and low values of the coefficient of determination R2, therefore evidencing the
small number of variables actually explained by the model. Additionally, it was found that a
considerable percentage of the independent variable average variation is explained by factors not
considered in the study. The latter may be related to the climate, the lack of compatibility between the
facade coating and the support, design misconception, building errors, and so on (Shohet and
Patciuk, 2006; Teo, 2005).
In conclusion, the model used revealed inadequacies for anticipating anomalies from the set of
causes considered; however, the model is interesting for understanding better how defects may vary
as a function of degradation factors.
Table 4 depicts the results of test F that validates the model in global terms but not each of its
parameters taken alone. So by analysing the results depicted in Table 4 it can be verified that the
models are adjusted to the data. Despite the low adequacy of prevision they are significant as pvalue=0 (sig.). It can be concluded that in each model at least one of the degradation factors has a
significant effect on the considered pathology variation.
Table 4 - ANOVA – F-Test
Anomaly
Cracks
Discoloured
covering
Covering
detachment
Spread of
vegetable and
micro organisms
Superficial
calcium
carbonate deposit
(efflorescence)
Humidity
Regression
Residual
Total
Regression
Sum of
Squares
12,065
44,388
56,453
df
Mean Square
F
Sig.
2
50
52
6,032
0,888
6,795
0,002
38,066
1
38,066
19,259
0,000
100,802
138,868
51
52
1,977
9,954
2
4,977
14,146
0,000
17,593
27,547
50
52
0,352
Residual
Total
Regression
Residual
Total
Regression
Residual
Total
Regression
Residual
Total
Regression
Residual
Total
Multiple-linear regression model: building evaluation index - anomalies
The multiple-linear regression model aims at estimating the influence of the predictors’ variables in
the evaluation index of the buildings’ external envelope. This model was used for measuring the
relation between the dependent variable – evaluation index of the sample buildings’ external envelope
– and the set of independent variables concerning the anomalies observed on the buildings’ facades
(level of degradation of blade cracking, covering discolouring, covering detachment, dark spots mainly
due to micro-organism culture and dirt remains, efflorescence spots and moisture spots) the level of
degradation of windows, roof and drainage system.
The multiple linear regression was obtained by using the SPSS - Statistical Package for Social
Sciences, version 14.0 for Windows, and by testing the forward, backward and stepwise selections.
Results were analysed and compared leading to the best adjusted model (Table 5).
Table 5 – Buildings evaluation index variation model: linear regression statistic values and
coefficients (Forward and Stepwise selection)
Model
Variable
Model 4
(Constant)
Discoloured
covering
Cracks
Frameworks
Roof
Unstandardized
regression
Coefficients
- 2,443
Standard
Error
Standardized
Coefficient
0,798
T
Value
Sig.
R2aj
-3,061
0,004
0,750
0,405
0,055
0,568
7,349
0,000
0,501
0,417
0,171
0,097
0,113
0,080
0,371
0,264
0,166
5,187
0,676
0,149
0,000
0,001
0,037
Note: Dependent variable: Building Evaluation Index (EI).
SPSS, gives the p-value associated to the F-test (F-Snedecor) statistical to (n-p-1) degree of
freedom. As for the obtained results, it can be verified that the F-test significance level is 0, less than
0.05 (p-value ≤ α). It can be concluded that in this model at least one of the predictor variables has a
significant effect on the dependent variable variation (Table 6). So it can be stated that these models,
adjusted to the data, are significant (Maroco, 2003).
Table 6 - ANOVA – F-Test
Model
Model 4
Regression
Residual
Total
Sum of
Squares
79,036
23,718
102,755
df
Mean Square
F
Sig.
4
48
52
19,759
0,494
39,987
0,000
It was tested whether in the model, all or only some of the independent variables influence the
dependent variable as following.
The hypothesis is: H0: βi = k vs H1: βi ≠ k, (i =1, …, p).
This was verified by the statistic of t-Student test that is valid for each of the variables acting
individually, but it is not valid when extrapolating to find if more than one of the variables have
influence acting simultaneously on the independent variable. This statistic with (n-p-1) degrees of
freedom is the probability p-value and has as rule of rejection H 0 if p-value ≤ α. So a significance level
of α/p = 0.01 (Bonferroni correlation) must be considered and not the α value (Maroco, 2003). The
obtained results confirmed that in this model all the independent variables have a significance level
less than 0.05 consequently p-value ≤ α. Accordingly all of them significantly affect the value of EI.
The same can not be concluded with the Bonferroni correction, as verified, because the roof
degradation corresponding variable is not significant in the model as its p-value = 0.037 is less than
0.05 but higher than 0.01, as shown in Table 7.
Considering the determination coefficient (R2) and the adjusted one (R2aj) it can be stated that in the
model R2aj = 0.750. Accordingly 75.0% of the Y total variability in IA is explained by the independent
variables present in the adjusted linear regression model (by the degradation level of the discolouring
covering, covering cracks, frameworks and roof).
Regarding the population inference it is necessary to test whether the adjusted model is significant.
The ANOVA (Analysis of Variance) regression, allows the testing of the hypotheses of H0: β1 = β2 = …
= βp = 0 vs  i : H1: βi ≠ 0, (i =1, …, p), equivalent to H0: ρ2 = 0 vs H1: ρ2 ≠ 0. As the obtained value of
F = 39.987 (Table 6) with 4 and 48 degrees of freedom and this statistic has associated a p-value = 0
(sig.), the H0 is rejected in favour of H1, so the model is adjusted.
Table 7 – Multiple liner regression: selected model
Variable
Model
(Constant)
Discoloured
covering
Cracks
Frameworks
Roof
Unstandardized
regression
Coefficients
-2,443
Standard
Error
Stand.
Coeff.
0,798
T
Value
Sig.
R2aj
-3,061
0,004
0,750
0,405
0,055
0,568
7,349
0,000
0,501
0,417
0,171
0,097
0,113
0,080
0,371
0,264
0,166
5,187
3,676
2,149
0,000
0,001
0,037
The adjusted model general equation that indicates the expected EI value can be given:
EI  2.443  0.405 X 1  0.501X 2  0.417 X 3  0.171X 4 
(1)
where:
EI – Building evaluation index
X1 – Discoloured covering degradation level
X2 – Cracks degradation level
X3 – Framework degradation level
X4 – Roof degradation level
ε – Residual random variable
Statistically to verify if all the independent variables make the same contribution to the model, if all of
them have a significant effect on the EI prediction, it is necessary to use the standardized regression
coefficients, also known as β coefficients, illustrated in Table 7 (Maroco, 2003). According to analysis
of these coefficients, the X1, X2 and X3 variables set the greatest relative contributions to explain the
EI behaviour in the model. The high contribution of X1 variable is explained by the greater incidence of
this pathology in the set of buildings considered. On the other hand the degradation that variable X 4
indicates is only registered when the inhabitants have effective dissatisfaction in relation to
waterproofing failures. The roof degradation aspects do not have an important influence on the
degradation of this variable in the analysed set of buildings, either due to the difficulty of observing
them or because of the minor importance given them by the inhabitants. The roof waterproofing
failure is not a generalized anomaly in this set as are the other observed anomalies. The fact of the
framework elements displaying severe anomalies with regard to waterproofing and air permeability, in
almost all the elements of the set, makes a significant contribution to the EI determination.
The obtained equation (1) represents a model for determining the global evaluation index of the
buildings, from the evaluation of four degradation relevant aspects of their envelope. In this way it is
possible to reduce from 9 to 4 the evaluation variables. This equation predicts the measure of
influence of the predictors’ variables on the EI. It has, as support, the degradation level of each
anomaly/pathology obtained through the visual survey of each building. So this model was developed
under the DL influence of the set of buildings. Its equation expresses the envelope performance of
this set of buildings in function of the degradation level of the performance of the most influential
variables, in accordance with the visual survey carried out.
In future research projects with larger sets it could be possible to test and even improve on the model.
CONCLUSIONS
The graduation scale and the developed models are innovative. The graduation scale is a
fundamental tool to characterize the degradation level of the housing park envelope. This is
considered indispensable to the EI and DL building’s assessment.
The developed models are of great interest because through the degradation level of a small number
of the envelope requirements an evaluation index can be achieved. Accordingly with this index the
envelope conservation, repair and rehabilitation costs can be predicted.
They also contribute to characterising the conservation state of the housing park which will permit the
estimation of the degradation state if no conservation, repair or rehabilitation action be taken.
REFERENCES
AIJ (1993). The English Edition of Principal Guide for Service Life Planning of Buildings. Architectural
Institute of Japan.
ALBATROS (2005). Mertz, C.; Flourentzou, F.; Gay, J.B. Méthode d´aide à la décision intégrant les
enjeux du développement durable en phase de planification d´un project de construction publique,
available at http://www.eco-bau.ch/francais/.
AQC (2006). Qualité, progressons ensemble. Bilan 1995-2005. Agence Qualité Construction.
Observatoire
de
la
Qualité
de
la
Construction,
available
at
http://www.qualiteconstruction.com/webzine/default. asp?main=38.
Balaras C.; Droutsa K.; Daskali E.; Kontoyiannidis S. (2005). Deterioration of European apartment
buildings. Energy and Buildings, 37. 515-527. Elsevier, available at www.sciencedirect.com.
Bluyssen, Philomena M. (2000). EPIQR and IEQ: indoor environment quality in European apartment
buildings. Energy and Buildings, 31. 103-110. February 2000. Elsevier, available at b-on – knowledge
library online.
BRE (1988). Common Defects in Low-Rise Traditional Housing. Digest 268. Building Research
Establishment. Garston.
Chamosa, J.V; Ortiz, J.R. (1984). Patologia de la construcción en España: aproximacion estadistica.
Informes de la Construcción, 36 nº1. 364. Octubre. Madrid.
CIB W080 (2006). Prediction of Service Life for Buildings and Components. CIB Report: Publication
310. State of the art Report on Failure Modes Effects and Criticality Analysis. Research for and
Application to the Building Domain, March.
CQF (1997). Database analysis – Report 4. Construction Quality Forum. April 1997. Garston.
EPIQR (2004). Les principes de la méthode. Available at http://www.epiqr.ch/outils_epiqr.html, data
de acesso [05/11/04].
Gaspar, P. L.; Brito, J. (2005). Assessment of the overall degradation level of an element, based on
the field data. 10th International Conference on Durability of Building Material and Components.
Paper TT8 – 68. 17-20 April. Lyon. France.
Gaspar, P. L. et al. (2006). Técnicas de Diagnóstico e Classificação de Fissuração em Fachadas
Rebocadas. PATORREB 2006. 541-550. FEUP. Porto.
HAPM (1997). Housing Association Property Mutual Feedback from Data 1991-1994. Technical Note
nº 7. London.
Harris, Samuel Y. (2001). Building Pathology: Deterioration, Diagnostics, and Intervention. John Wiley
& Sons. EUA.
Henriques, Fernando M. A. (2001). A noção de qualidade em edifícios. Comunicação ao Congresso
Nacional da Construção. Instituto Superior Técnico. 17-19 Dezembro. Lisboa. Disponível em
http://www.dec.fct.unl.pt/seccoes/smtc/pub7.pdf.
Hovde, Jostein (2004). FACTOR METHODS FOR SERVICE LIFE PREDICTION State of the art. CIB
W080/RILEM 175 SLM: Service Life Methodologies Prediction of Service Life for Buildings and
Components. Task Group Performance Based Methods for Service Life Prediction, available at
http://www.auspebbu.com/files/PeBBuServLife_March04_Pub294.pdf.
INE (2001-a). Censos 2001: resultados definitivos: XIV recenseamento geral da população: IV
recenseamento geral da habitação. 1º Volume. Lisboa: INE.
INE (2001–b). Análise de resultados, available at www.ine.pt/prodserv/Quadros/396/218/001/pdf.
INE (2003). Lisboa.
Lair, J.; Chevalier, J.L.( 2002). Failure Mode Effect and Criticality Analysis for Risk Analysis (design)
and Maintenance Planning (exploitation). 9th International Conference on Durability of Building
Material and Components, 2002, Brisbane, Australia, March, Paper 032, p. 17-21.
LNEC (2004). 3ª Análise Retrospectiva do Parque Antecedentes, Metodologia e Conceitos: Censos
2001: XIV recenseamento geral da população: IV recenseamento geral da habitação.Habitacional
Financiado pelo INH anos de 1995 a 1998. Relatório 239/04 – NAU. Lisboa: LNEC.
Lounis et al. (1998). Effective decision-making tools for roofing maintenance management. Originally
published in the proceedings of the First International Conference on New Information Technologies
for Decision Making in Construction. 425 – 436. Edmont T. Miresco, ed. Montreal, available at
http://citeseer.ist.psu.edu/cache/papers/cs/31131/http:zSzzSzirc.nrccnrc.gc.cazSzfulltextzSznrcc42831.pdf/lounis98effective.pdf.
MAEC (2006). Método de avaliação do estado de conservação de edifícios. Instruções de aplicação e
ficha de avaliação. Ministério das Obras Públicas, Transportes e Comunicações. Outubro de 2006.
LNEC. Lisboa.
Maroco, João (2003). Análise estatística com utilização do SPSS. Edições Sílabo. Lisboa.
Marteinsson, B.; Jónsson, B. (1999). Overall Survey of Buildings – performance and maintenance. 8th
International Conference on Durability of Building Material and Components.1634 – 1654. May 30 June 3. Vancouver. Canada.
Recommendations (2000). Recommendations For The 2000 Censuses of Population And Housing In
The EEC Region. United Nations Statistical Commission and Economic Commission for Europe
Conference of European Statistians. Economic Commission for Europe Committee on Human
Settlements. Statistical Standards and Studies – Nº. 49. Prepared by the United Nations Economic
Commission for Europe and the Statistical Office of the European Communities. United Nations.
1998.
New
York
and
Geneva,
available
at
http://europa.eu.int/estatref/info/sdds/en/hrst/hrst_cs_recommendations.pdf.
Rodrigues, M. Fernanda S. (2008). Estado de Conservação de Edifícios de Habitação a Custos
Controlados. Índice de avaliação e metodologia para a sua obtenção, Tese de Doutoramento,
Universidade de Aveiro, Aveiro.
Sarja, Asko et al. (2005). Lifetime Engineering of Buildings and Civil Infrastructures. European guide
for life time design and management of civil infrastructures and buildings. Project Cluster Lifetime.
Technical Research Centre of Finland, VTT. VTT Building and Transport, available at
http://lifetime.vtt.fi/delivrables_of_project_cluster/european_guide.pdf.
Socotec (2002). Guide Socotec de la Maintenance et de la Réhabilitation. Fiche 5.04. Groupe
Moniteur.
Shohet, Igal M.; Paciuk, Monica (2006) Service life prediction of exterior cladding components under
failure conditions. Construction Management and Economics, 24, 131-148.
Teo, Evelyn Ai Lin; Harikrishna, N. (2006). A quantitative model for efficient maintenance of plastered
and painted facades. Construction Management and Economics, 24. 1283-1293. Ed. Routledge.
Taylors and Francis Group, available at b-on – knowledge library online.
Teo, Evelyn Ai Lin (2005). An assessment of factors affecting the service life of external paint finish
on plastered facades. 10th International Conference on Durability of Building Material and
Components, Lyon, France, April, Paper TT4 - 152, p. 17-20.
Trotman, P. (1994). An examination of the BRE Advisory Service database compiled from property
inspections. Paper presented of the Dealing with Defects in Building Symposium, CIB/ICITECNR/DISET. 27-30 September. Varena. Italy.
Watt, David S. (1999). Building Pathology – Principles & Practice. Blackwell Science. London.