DEVELOPMENT OF A BAYESIAN BASED DESIGN-FOR-MAINTINABILITY DECISION SUPPORT FRAMEWORK

Abstract

The lack of Design for Maintainability (DfM) leads to avoidable building component defects and unproductive building operations. This study reports on the development of a building design assessment framework which predicts maintainability impacts of design alternatives. A set of maintainability criteria were identified and their interdependencies explored with an impact relation map developed using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. A mixed method approach was used in this study where 1372 DfM benchmarks from 404 national and international building standards were analysed qualitatively to identify benchmarks relating to 123 critical building defects. Five Bayesian Belief Networks (BBN) were developed using defect data and expert judgements from multiple case studies. Results provided monetary quantifications indicating the complex relationships between DfM benchmarks and building defects to great effect. This novel consequence-based framework aids in building more maintainable building systems which are more productive in terms of materials, labour and cost.