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Empowering construction safety culture through artificial intelligence: risk-based approach.

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Τίτλος: Empowering construction safety culture through artificial intelligence: risk-based approach.
Συγγραφείς: Bari, Aadil, Arshi, Oroos, Mondal, Surajit
Πηγή: Smart Construction & Sustainable Cities; 6/11/2026, Vol. 4 Issue 1, p1-24, 24p
Θεματικοί όροι: Artificial intelligence, Internet of things, Risk assessment, Big data, Computer vision, Robotics, Safety, Construction industry safety, Drone aircraft, Safety regulations, Machine learning
Περίληψη: This article examines the part of AI in promoting part of safety in construction via a risk-based lens. Although the literature has provided insight into the use of AI in fragmented ways, a comprehensive framework to help with the integration of AI-led risk assessment into dynamic safety management was not available. This literature review pulls together contemporary AI technologies—machine learning, computer vision, and IoT and their application to risk prediction, hazard detection, and safety compliance monitoring. The principal finding is the proactive aspect of safety management empowered by real-time data analytics and predictive modeling; however, the findings do highlight issues with regards to data quality, explainability, and ethical use. The results of this review offer a first-structured direction forward towards stimulus so that to conceive more complete research, and tackle the consequences to the practitioners that may wish to realize intelligent safety systems and enhance safety culture in building. [ABSTRACT FROM AUTHOR]
Copyright of Smart Construction & Sustainable Cities is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Robotics%22">Robotics</searchLink><br /><searchLink fieldCode="DE" term="%22Safety%22">Safety</searchLink><br /><searchLink fieldCode="DE" term="%22Construction+industry+safety%22">Construction industry safety</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22Safety+regulations%22">Safety regulations</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Data: This article examines the part of AI in promoting part of safety in construction via a risk-based lens. Although the literature has provided insight into the use of AI in fragmented ways, a comprehensive framework to help with the integration of AI-led risk assessment into dynamic safety management was not available. This literature review pulls together contemporary AI technologies—machine learning, computer vision, and IoT and their application to risk prediction, hazard detection, and safety compliance monitoring. The principal finding is the proactive aspect of safety management empowered by real-time data analytics and predictive modeling; however, the findings do highlight issues with regards to data quality, explainability, and ethical use. The results of this review offer a first-structured direction forward towards stimulus so that to conceive more complete research, and tackle the consequences to the practitioners that may wish to realize intelligent safety systems and enhance safety culture in building. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Smart Construction & Sustainable Cities is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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              Text: 6/11/2026
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