Service life prediction of façade paint coatings in old buildings

doi:10.1016/j.conbuildmat.2011.10.057

Construction and Building Materials

Volume 29, April 2012, Pages 394-402

https://doi.org/10.1016/j.conbuildmat.2011.10.057 Get rights and content

Abstract

The financial resources available for infrastructure maintenance and rehabilitation are always limited, creating a need for efficient resource management and for the ability to predict maintenance actions throughout the infrastructure service period. In the context of bridge management, management systems have been developed and are already implemented around the world. Due to a much larger variety of maintenance problems and to a reduced number of buildings per owner, building management systems are still quite rare. In fact efficient methodologies for service life prediction of building materials still need to be developed.

This paper presents and tests a service life prediction methodology, based on the statistical analysis of anomalies obtained from the inspection of in-use buildings and the corresponding degradation curves obtained from deterministic models. The method was applied to the service life prediction of paint coatings in old buildings’ façades and considers the quantification of the defect associated to paint peeling. The influence of five construction/environment degradation factors on the behavior of paint coatings over time is also analyzed.

Highlights

▸ Service life prediction methodology for façade paint coatings in old buildings. ▸ Data collected from inspection of in-use buildings, five degradation factors studied. ▸ The paint peeling defect is studied, being quantified for each building. ▸ Paint films with thickness above 400 μm show improved performance over thinner films. ▸ West facing coatings present worst performance when compared to other orientations.

Introduction

Today there is a strong motion towards a more efficient use of resources in every sector of human activity, with the goal of achieving and maintaining a human “sustainable development” [1]. The construction industry is one of the most important industries in this context, given the influence that the built environment has on the life of populations and on the economical development of nations. In fact, the economic activity housed in built environments is significantly influenced by inputs given during the design, construction and maintenance phases of these environments [2].

Within the construction industry, the rehabilitation and maintenance strategies must also optimize the social and economic benefits of the built environment. In the present economical situation where the funds available for such actions are always very limited, there is a growing need to plan and prioritize the necessary maintenance works. The planning of such works is achieved by predicting the moments when the critical elements of the built assets reach degradation levels that exceed acceptable values. To be able to make such predictions, developments must be made concerning the methodologies for service life prediction of building materials and components.

To solve this situation systems for bridge management were developed during the last 15 years and are already implemented around the world [3]. Nevertheless, due to a much larger variety of maintenance problems and to a reduced number of buildings associated to each owner, building management systems are still quite rare.

The research here presented [4] aims to contribute to such development, by exploring and testing a service life prediction methodology based on the statistical analysis of data obtained from the observation of long term degradation of buildings. The method is achieved through the inspection of in-service buildings, and it is applied to the specific case of service life prediction of paint coatings in old building façades. In a first step the inspections quantified the degradation level associated to the defect of the paint peeling in several buildings. The influence of five construction/environment degradation factors on the paint coating performance, over time, was also assessed during the inspections. Afterwards, using a statistical data analysis, degradation curves were obtained simulating the paint performance over time, and allowing for service life prediction considering a pre-defined maximum level of degradation.

Section snippets

Service life prediction methodologies

The development of service life prediction methodologies was greatly influenced by the work of several technical committees, belonging to entities such as CIB (CIB W80), RILEM (RILEM 71-PSL, 100-TSL, 175-SLM) or ISO (ISO TC 59 SC 14) [5]. From the joint work of the CIB W80 and RILEM 71-PSL technical committees, an outline of a general methodology for service life prediction of building materials and components was developed, and some research needs in that area were identified [6], [7].

From the

Problem definition

In this study, the methodology of service life prediction was applied to the façades of old buildings and to the particular defect associated to paint peeling, considering its performance over time. In this type of degradation, the effect of the following five degradation factors (DF) was considered: (i) DF1: coating thickness; (ii) DF2: paint binder; (iii) DF3: paint surface texture; (iv) DF4: substrate surface preparation; and (v) DF5: façade solar orientation. The data for the study was

Data analysis

The inspection data is then a sample of points (each building), each representing the relation between the time elapsed since the last maintenance action (which in the present case is the application of the last paint coating) and the value of the painting degradation. The degradation was the extension of the peeled coating area, expressed as a percentage of the total painted area in the façade.

Based on the previous data, the degradation modeling was analyzed considering deterministic models

Global degradation curves

In Fig. 11 the total data is presented, along with the associated degradation curves (the associated equations are shown in Table 5 ). A significant dispersion in the data is observed as it considers data from different types of coatings which present different performances over time. In fact, the dispersion in this global data reflects the different influences that the degradation factors have on the performance over time of paint coatings.

The CMSE values presented in Table 4 indicate that

Conclusions

Aiming to contribute to the development of building management systems, a service life prediction methodology is presented, for the service life prediction of façade paint coatings in old buildings. The method was tested through its application to a sample of 100 buildings and considering the peeling as the coating defect.

With the use of degradation curves it was possible to observe the influence of the five degradation factors in the performance over time of paint coatings. The research used

Acknowledgments

The authors wish to acknowledge Isabel Eusébio, Maria Paula Rodrigues and Helena Silva from LNEC, as well as Maria Amélia Dionísio from CEPGIST for their support in the development of some of the experimental tests.

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