An artificial neural network approach in service life prediction of building components in Malaysia based on local environment and building service load

Abstract

The degradation of building and its components are influenced by whole set of factors such as environmental degradation agents, quality of material, protective treatment, design of building, quality of work and maintenance. Selection of suitable materials for the building components can prolong the service life of particular building components and in certain cases require less maintenance and replacement activity. Emphasis on material characterisations at the design stage is limited because most of the time great emphasis is given on delivering with lowest initial building cost rather than lowest life cycle cost. In this study, an artificial neural network is used to predict the service life of building materials with the basis study on deterioration of building components affected by its surrounding environment and factors that accelerate its aging process. The advantages of artificial neural network is employing as a prediction tool. The back-propagation learning algorithm is used as learning model. The environment load factors, workmanship, design, usage and level of maintenance are used as input variables in training process of the neural network model. The results are encouraging and potentially useful for further application of the service life prediction