TY - JOUR
T1 - Artificial intelligence and smart vision for building and construction 4.0 : machine and deep learning methods and applications
AU - Baduge, S.K.
AU - Thilakarathna, S.
AU - Perera, J.S.
AU - Arashpour, M.
AU - Sharafi, Pejman
AU - Teodosio, B.
AU - Shringi, A.
AU - Mendis, P.
PY - 2022
Y1 - 2022
N2 - This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management and health monitoring; and durability, life cycle analysis, and circular economy. This paper presents a unique perspective on applications of AI/DL/ML in these domains for the complete building lifecycle, from conceptual stage, design stage, construction stage, operational and maintenance stage until the end of life. Furthermore, data collection strategies using smart vision and sensors, data cleaning methods (post-processing), data storage for developing these models are discussed, and the challenges in model development and strategies to overcome these challenges are elaborated. Future trends in these domains and possible research avenues are also presented.
AB - This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management and health monitoring; and durability, life cycle analysis, and circular economy. This paper presents a unique perspective on applications of AI/DL/ML in these domains for the complete building lifecycle, from conceptual stage, design stage, construction stage, operational and maintenance stage until the end of life. Furthermore, data collection strategies using smart vision and sensors, data cleaning methods (post-processing), data storage for developing these models are discussed, and the challenges in model development and strategies to overcome these challenges are elaborated. Future trends in these domains and possible research avenues are also presented.
UR - https://hdl.handle.net/1959.7/uws:74996
U2 - 10.1016/j.autcon.2022.104440
DO - 10.1016/j.autcon.2022.104440
M3 - Article
SN - 0926-5805
VL - 141
JO - Automation in Construction
JF - Automation in Construction
M1 - 104440
ER -