A framework for developing machining learning models for facility life-cycle cost analysis through BIM and IoT
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This thesis presents a research project that developed a machine learning-enabled facility life-cycle cost analysis (LCCA) framework using data provided by Building Information Models (BIM) and the Internet of Things (IoT). First, a literature review and a questionnaire survey were conducted to determine the independent variables affecting the facility life-cycle cost (LCC). The potential data sources were summarized, and a data integration process introduced. Then, the framework for developing machine learning models for facility LCCA was proposed. A domain ontology for machine learning-enabled LCCA (LCCA-Onto) was developed to encapsulate knowledge about LCC components and their roles in relation to sibling ontologies that conceptualize the LCCA process. A series of experiments were conducted on a university campus to demonstrate the application of the proposed machine learning-enabled LCCA framework. Finally, the author’s vision of the future smart built environment was discussed.