Version 1
: Received: 18 September 2024 / Approved: 19 September 2024 / Online: 20 September 2024 (11:16:11 CEST)
How to cite:
Ait Lamkademe, H.; Naddami, A.; Choukri, K. Predictive Analysis of Causal Factors Influencing Occupational Accidents in Construction Workplaces Using a Machine Learning Unified Data Model. Preprints2024, 2024091611. https://doi.org/10.20944/preprints202409.1611.v1
Ait Lamkademe, H.; Naddami, A.; Choukri, K. Predictive Analysis of Causal Factors Influencing Occupational Accidents in Construction Workplaces Using a Machine Learning Unified Data Model. Preprints 2024, 2024091611. https://doi.org/10.20944/preprints202409.1611.v1
Ait Lamkademe, H.; Naddami, A.; Choukri, K. Predictive Analysis of Causal Factors Influencing Occupational Accidents in Construction Workplaces Using a Machine Learning Unified Data Model. Preprints2024, 2024091611. https://doi.org/10.20944/preprints202409.1611.v1
APA Style
Ait Lamkademe, H., Naddami, A., & Choukri, K. (2024). Predictive Analysis of Causal Factors Influencing Occupational Accidents in Construction Workplaces Using a Machine Learning Unified Data Model. Preprints. https://doi.org/10.20944/preprints202409.1611.v1
Chicago/Turabian Style
Ait Lamkademe, H., Ahmed Naddami and Karim Choukri. 2024 "Predictive Analysis of Causal Factors Influencing Occupational Accidents in Construction Workplaces Using a Machine Learning Unified Data Model" Preprints. https://doi.org/10.20944/preprints202409.1611.v1
Abstract
Occupational incidents in construction workplaces present a persistent challenge, with severe consequences for both workers and project outcomes. This study aims to examine the specific causal factors contributing to these incidents, focusing on the interaction between worker-related, environmental, and procedural factors. The objective is to identify key contributors to workplace accidents and develop a unified predictive model to mitigate future risks. To achieve this, we employed a machine learning approach on a comprehensive accidentology dataset collected from contractors across multiple construction sites. The dataset includes variables such as worker ex-perience, environmental conditions, adherence to safety protocols, and more. By analyzing direct, indirect, and root causes, the methodology uncovers hidden patterns and interdependencies that traditional analysis might overlook. The study’s findings indicate that worker experience and environmental factors are the most significant contributors to incident occurrence, with a clear interaction effect between these variables. The results not only confirm previous research but also offer enhanced predictive capabilities for future safety measures. This research demonstrates the value of machine learning in generating data-driven insights, ultimately aiding in the develop-ment of targeted interventions to improve safety standards in the construction industry.
Keywords
machine learning; predictive analytics; data mining; safety; occupational accidents; construction; workplace; artificial intelligence; big data; incidents
Subject
Engineering, Safety, Risk, Reliability and Quality
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
