A Nonparametric Statistical Model for the Selection of Significant Variables Acting in the Deterioration of Built Façades

Abstract

Serrat and Gibert (2011) introduced the use of survival analysis techniques for the study of time-to-failure in the facades of a building stock. Those techniques have been implemented in a predictive system that incorporates the inspection methodology together with the statistical methodology. The system proposes a network of urban laboratories for building research analysis and information with the aim of modelling the degradation time based on the information from all the cities participating in the project, from a multiscale perspective. The approach is population based, that is from the manager of the building stock point of view, in order to obtain information on the evolution of the stock across time, and to help the manager with decision making process on global maintenance strategies. However, for an efficient decision making it is crucial to determine those covariates -like materials, morphology and characteristics of the facade, orientation or environmental conditions- that play a significant role in the progression of different failures along the façade service life. The proposed platform also incorporates an open source GIS plugin that includes survival and test moduli that allow the investigator to model the time until a damage when taking into account the variables collected during the inspection process. The aim of the paper is to introduce the methodology and the modelling strategy for the deteriorating process of an urban front. The contribution will be illustrated with a case study located in the city of L’Hospitalet de Llobregat (Barcelona, Spain) in which more than 14,000 facades have been inspected and analyzed.

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