对机器学习和人工智能在建筑行业项目生命周期中的应用进行了广泛的研究

IF 6.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Nervana Osama Hanafy, Nourhan Osama Hanafy
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引用次数: 0

摘要

人工智能(AI)和机器学习(ML)在建筑行业的整合在过去十年中获得了越来越大的动力。本研究提出了一项系统的文献综述,旨在评估人工智能/机器学习技术在建设项目生命周期的五个关键阶段的部署:规划、设计、施工、运营和维护以及拆除。该审查遵循PRISMA的指导方针,采用三阶段过滤过程来分析2013年至2023年的出版物。研究结果表明,规划和施工阶段的应用最为广泛和成熟,特别是在成本估算、风险分析、安全管理和调度优化方面。相比之下,在拆除和入住后阶段的采用仍然有限。该研究还指出了主要挑战,包括数据质量、集成障碍和道德考虑。通过映射AI/ML在生命周期阶段的使用,本文为进一步的学术研究和智能技术在建筑中的实际实施提供了结构化的基础
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An extensive examination of uses of machine learning and artificial intelligence in the construction industry’s project life cycle
The integration of Artificial Intelligence (AI) and Machine Learning (ML) in the construction industry has gained increasing momentum over the past decade. This study presents a systematic literature review aimed at evaluating the deployment of AI/ML technologies across the five key phases of the construction project life cycle: planning, design, construction, operation and maintenance, and demolition. The review follows PRISMA guidelines and employs a three-stage filtering process to analyze publications from 2013 to 2023. Findings indicate that the planning and construction phases feature the most extensive and mature applications, particularly in cost estimation, risk analysis, safety management, and scheduling optimization. In contrast, adoption in demolition and post-occupancy phases remains limited. The study also identifies major challenges including data quality, integration barriers, and ethical considerations. By mapping AI/ML use across lifecycle stages, this paper provides a structured foundation for further academic inquiry and practical implementation of intelligent technologies in construction
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来源期刊
Energy and Buildings
Energy and Buildings 工程技术-工程:土木
CiteScore
12.70
自引率
11.90%
发文量
863
审稿时长
38 days
期刊介绍: An international journal devoted to investigations of energy use and efficiency in buildings Energy and Buildings is an international journal publishing articles with explicit links to energy use in buildings. The aim is to present new research results, and new proven practice aimed at reducing the energy needs of a building and improving indoor environment quality.
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