Artificial Intelligence in Construction Project Management: A Systematic Literature Review of Cost, Time, and Safety Management

Artificial intelligence (AI) has become the leading technology for digital transformation in various industries. However, the digitalization of construction project management (e.g., cost, time, and safety) in the context of AI technology implementation is still limited. Therefore, this paper aims to conduct a systematic literature review of AI technologies in construction project cost, time, and safety management, and identify mainstream application areas, cross-domain synthesis, challenges, research gaps, and future research directions. By adopting the PRISMA approach, a systematic literature review was conducted to retrieve 392 articles from the Scopus database. The results presented mainstream application areas of construction project cost (i.e., cost estimation, cost prediction, cost index forecasting, cost control, cost optimization), time (i.e., planning and scheduling, delay risk prediction, time optimization, cycle time prediction), and safety (i.e., workers’ safety monitoring, on-site safety monitoring, personal protective equipment (PPE) detection, safety report text analysis, fall risk monitoring, safety accident prediction, and safety hazard identification and risk assessment). Moreover, the cross-domain synthesis, challenges, and research gaps of AI technologies in construction project management were discussed. Based on these findings, this paper suggests future directions to extend research in this domain. This paper would contribute to the construction project management research domain by providing key application areas and useful research directions, thus promoting digital transformation in the sector.

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Publication Details

Journal
Buildings
Published
2026-03-07
DOI
https://doi.org/10.3390/buildings16051061
Primary Topic
Occupational Health and Safety Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial Intelligence in Construction Project Management: A Systematic Literature Review of Cost, Time, and Safety Management

Bilal Manzoor, Zhen‐Song Chen, Yuxiang Huang, Maxwell Fordjour Antwi-Afari et al.
Buildings
Occupational Health and Safety Research
article

Artificial Intelligence in Construction Project Management: A Systematic Literature Review of Cost, Time, and Safety Management

Bilal Manzoor, Zhen‐Song Chen, Yuxiang Huang, Maxwell Fordjour Antwi-Afari, Yingxin Gao
article en

Abstract

Artificial intelligence (AI) has become the leading technology for digital transformation in various industries. However, the digitalization of construction project management (e.g., cost, time, and safety) in the context of AI technology implementation is still limited. Therefore, this paper aims to conduct a systematic literature review of AI technologies in construction project cost, time, and safety management, and identify mainstream application areas, cross-domain synthesis, challenges, research gaps, and future research directions. By adopting the PRISMA approach, a systematic literature review was conducted to retrieve 392 articles from the Scopus database. The results presented mainstream application areas of construction project cost (i.e., cost estimation, cost prediction, cost index forecasting, cost control, cost optimization), time (i.e., planning and scheduling, delay risk prediction, time optimization, cycle time prediction), and safety (i.e., workers’ safety monitoring, on-site safety monitoring, personal protective equipment (PPE) detection, safety report text analysis, fall risk monitoring, safety accident prediction, and safety hazard identification and risk assessment). Moreover, the cross-domain synthesis, challenges, and research gaps of AI technologies in construction project management were discussed. Based on these findings, this paper suggests future directions to extend research in this domain. This paper would contribute to the construction project management research domain by providing key application areas and useful research directions, thus promoting digital transformation in the sector.

BuildingsVol. 16(5)
Hong Kong Polytechnic University (HK), Aston University (GB), University of Manchester (GB), Manchester School of Architecture (GB), Wuhan University (CN), Zhejiang University (CN)
Openalex Percentile: Top 36%
Occupational Health and Safety Research
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