Revolutionising construction safety: benefits of harnessing artificial intelligence tools for dynamic monitoring of safety compliance on construction projects in Nigeria

Abstract

Purpose

This paper evaluates the benefits of harnessing artificial intelligence (AI) tools for safety compliance on construction projects in Nigeria.

Design/methodology/approach

This study employed a specialised approach by combining qualitative and quantitative approach. The study carried out a brief systematic literature review (SLR) to identify the variables of the study. These variables were prepared in a questionnaire which was distributed among professionals within the Nigerian construction sector using purposive sampling. A total of 140 questionnaires were retrieved. The collected data were analysed using Relative Importance Index (RII), Ginni’s Mean (GM) and exploratory factor analysis (EFA).

Findings

The analysis revealed that all the identified benefits hold considerable importance, with an average RII of 0.86, with real-time monitoring as the most prominent advantage. However, using the GM which was 0.861, the study identified “mitigation of hazards on worksites” as the stationary benefit of AI in safety compliance.

Research limitations/implications

The study was conducted exclusively within Nigeria’s Federal Capital Territory, using a cross-sectional survey approach.

Practical implications

The results will be valuable for professionals and practitioners in the Nigerian construction sector, as they will acquire insights into the potential advantages of utilising AI tools for monitoring of safety compliance on construction projects.

Originality/value

The study adopted a robust approach by identifying the stationary benefit using the GM in combination with RII and EFA.

Citation

Adamu, I.I., Okanlawon, T.T., Oyewobi, L.O., Shittu, A.A. and Jimoh, R.A. (2024), "Revolutionising construction safety: benefits of harnessing artificial intelligence tools for dynamic monitoring of safety compliance on construction projects in Nigeria", International Journal of Building Pathology and Adaptation, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJBPA-02-2024-0050