TY - GEN
T1 - Machine Learning Applications in Facility Life-Cycle Cost Analysis
T2 - ASCE International Conference on Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, i3CE 2019
AU - Gao, Xinghua
AU - Pishdad-Bozorgi, Pardis
AU - Shelden, Dennis R.
AU - Hu, Yuqing
N1 - Publisher Copyright:
© 2019 American Society of Civil Engineers.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85068781307&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068781307&partnerID=8YFLogxK
U2 - 10.1061/9780784482445.034
DO - 10.1061/9780784482445.034
M3 - Conference contribution
AN - SCOPUS:85068781307
T3 - Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
SP - 267
EP - 274
BT - Computing in Civil Engineering 2019
A2 - Cho, Yong K.
A2 - Leite, Fernanda
A2 - Behzadan, Amir
A2 - Wang, Chao
PB - American Society of Civil Engineers (ASCE)
Y2 - 17 June 2019 through 19 June 2019
ER -