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Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications
被引:427
作者:
Baduge, Shanaka Kristombu
[1
]
Thilakarathna, Sadeep
[1
]
Perera, Jude Shalitha
[1
]
Arashpour, Mehrdad
[2
]
Sharafi, Pejman
[3
]
Teodosio, Bertrand
[4
]
Shringi, Amkit
[2
]
Mendis, Priyan
[1
]
机构:
[1] Univ Melbourne, Dept Infrastructure Engn, Melbourne, Vic 3010, Australia
[2] Monash Univ, Dept Civil Engn, Clayton, Vic 3800, Australia
[3] Western Sydney Univ, Sch Engn, Design & Built Environm, Parramatta, NSW 2150, Australia
[4] Victoria Univ, Coll Engn & Sci, Footscray, Vic 3011, Australia
关键词:
Artificial intelligence;
Machine learning;
Deep learning;
Automation;
Internet of things;
Building information modelling;
Smart vision;
Convolution neural network;
Generative adversarial network;
Artificial neural network;
NEURAL-NETWORK PREDICTION;
PAVEMENT CRACK DETECTION;
SUPPORT VECTOR MACHINE;
HIGH-STRENGTH CONCRETE;
3D ASPHALT SURFACES;
DAMAGE DETECTION;
COMPRESSIVE STRENGTH;
ELASTIC-MODULUS;
TENSILE-STRENGTH;
LOAD PREDICTION;
D O I:
10.1016/j.autcon.2022.104440
中图分类号:
TU [建筑科学];
学科分类号:
0813 ;
摘要:
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.
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