A framework for developing machining learning models for facility life-cycle cost analysis through BIM and IoT

Building information models (BIM);Internet of Things (IoT);Life-cycle cost analysis (LCCA);Machine learning

Gao, Xinghua ; Pishdad-Bozorgi, Pardis Building Construction Shelden, Dennis R. Irizarry, Javier Kristanto, Andreas Chau, Duen Horng ; Pishdad-Bozorgi, Pardis

University:Georgia Institute of Technology

Department:Building Construction

关键词: Building information models (BIM);    Internet of Things (IoT);    Life-cycle cost analysis (LCCA);    Machine learning;   

Others  :  https://smartech.gatech.edu/bitstream/1853/62660/1/GAO-DISSERTATION-2019.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

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.