Статья

Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications

Shanaka Kristombu BadugeDepartment of Infrastructure Engineering, The University of Melbourne, Melbourne 3010, AustraliaSadeep ThilakarathnaDepartment of Infrastructure Engineering, The University of Melbourne, Melbourne 3010, AustraliaJude Shalitha PereraDepartment of Infrastructure Engineering, The University of Melbourne, Melbourne 3010, AustraliaMehrdad ArashpourDepartment of Civil Engineering, Monash University, Clayton 3800, AustraliaP. SharafiSchool of Engineering, Design and Built Environment, Western Sydney University, Parramatta, NSW 2150, AustraliaBertrand TeodosioCollege of Engineering & Science, Victoria University, Footscray, VIC 3011, AustraliaAnkit ShringiDepartment of Civil Engineering, Monash University, Clayton 3800, AustraliaPriyan MendisDepartment of Infrastructure Engineering, The University of Melbourne, Melbourne 3010, Australia

2022en

ABI

Аннотация

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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Идентификаторы

DOI: 10.1016/j.autcon.2022.104440

Цитирования и источники

Цитирований: 3Использованных источников: 0

Показатели — AkademScholar