AI for Improving Construction Safety: A Systematic Literature Review

9 pages•Published: December 11, 2023

Abstract

Artificial intelligence (AI) has been adopted and applied in many fields and has now become one of the emerging technologies in the automation of the construction industry, which has gained a lot of attention from researchers in recent years. Much research work has been done on applying AI to improve construction safety. However, the current research work is focused on improving safety in separate individual construction tasks and the developed models lack real-world applications. Therefore, a systematic literature review has been conducted on the use of AI including machine learning and deep learning in improving safety in construction practice. After the review of the existing literature, the current applications and practices of AI are identified and classified. This will help in developing a new generalized framework that focuses on the entire construction process for improving safety. The limitations and the potential improvements in the existing AI techniques have been identified which will benefit future studies.

Keyphrases: artificial intelligence (ai), construction safety, deep learning, machine learning

In: Tom Leathem, Wes Collins and Anthony Perrenoud (editors). Proceedings of 59th Annual Associated Schools of Construction International Conference, vol 4, pages 354-362.

Links:	https://easychair.org/publications/paper/gqKr
	https://doi.org/10.29007/jtkn

BibTeX entry

@inproceedings{ASC2023:AI_Improving_Construction_Safety,
  author    = {Zhili Gao and Asad Sultan},
  title     = {AI for Improving Construction Safety: A Systematic Literature Review},
  booktitle = {Proceedings of 59th Annual Associated Schools of Construction International Conference},
  editor    = {Tom Leathem and Wes Collins and Anthony Perrenoud},
  series    = {EPiC Series in Built Environment},
  volume    = {4},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2632-881X},
  url       = {/publications/paper/gqKr},
  doi       = {10.29007/jtkn},
  pages     = {354-362},
  year      = {2023}}

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