BIM- and IoT-based data-driven decision support system for predictive maintenance of building facilities

Facility managers usually conduct reactive maintenance or preventive maintenance plans according to the condition of building components. However, reactive maintenance cannot prevent the failure of a component while preventive maintenance cannot predict the failure in advance, which leads to inefficient maintenance. Building information modelling (BIM) and internet of things (IoT) provide new opportunities to improve the efficiency of facility maintenance management. Even though significant efforts have been made on BIM and IoT applications in the architecture, engineering and construction/facility management (AEC/FM) industry, the exploration of BIM and IoT integration for FM is still at an initial stage. This chapter develops a BIM- and IoT-based data-driven predictive maintenance framework for facility management, which consists of the following four modules: condition monitoring and fault diagnosis module, condition assessment module, condition prediction module and maintenance rescheduling module. In this process, realtime data collected from IoT sensor network and historical maintenance records from FM system are used for condition prediction. Furthermore, two machine learning methods, namely support vector machine (SVM) and artificial neural network (ANN), are applied to predict the condition of critical equipment in the illustrative example to validate the feasibility of this framework. © 2019 by World Scientific Publishing Co. Pte. Ltd.