Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Machine Learning Applications in Facility Life-Cycle Cost Analysis: A Review
A large amount of resources are spent on constructing new facilities and maintaining the existing ones. The total cost of facility ownership can be minimized by focusing on reducing the facilities life-cycle costs (LCCs) rather than the initial design and construction costs. With the developments of machine learning in predictive analytics and the utilizing building systems that provide ubiquitous sensing and metering devices, new opportunities have emerged for architecture, engineering, construction, and operation (AECO) professionals to obtain a deeper level of knowledge on buildings’ LCCs. This paper provides a state-of-the-art overview of the various machine learning applications in the facility LCC analysis field. This paper aims to present current machine learning for LCC research developments, analyze research trends, and identify promising future research directions.
Machine Learning Applications in Facility Life-Cycle Cost Analysis: A Review
A large amount of resources are spent on constructing new facilities and maintaining the existing ones. The total cost of facility ownership can be minimized by focusing on reducing the facilities life-cycle costs (LCCs) rather than the initial design and construction costs. With the developments of machine learning in predictive analytics and the utilizing building systems that provide ubiquitous sensing and metering devices, new opportunities have emerged for architecture, engineering, construction, and operation (AECO) professionals to obtain a deeper level of knowledge on buildings’ LCCs. This paper provides a state-of-the-art overview of the various machine learning applications in the facility LCC analysis field. This paper aims to present current machine learning for LCC research developments, analyze research trends, and identify promising future research directions.
Machine Learning Applications in Facility Life-Cycle Cost Analysis: A Review
Gao, Xinghua (Autor:in) / Pishdad-Bozorgi, Pardis (Autor:in) / Shelden, Dennis R. (Autor:in) / Hu, Yuqing (Autor:in)
ASCE International Conference on Computing in Civil Engineering 2019 ; 2019 ; Atlanta, Georgia
Computing in Civil Engineering 2019 ; 267-274
13.06.2019
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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