Citation

Koc K, Ekmekcioğlu, Gurgun AP. Safety Sci. 2023; 164: e106157.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.ssci.2023.106157

PMID

unavailable

Abstract

Despite significant progress has been made in safety management practices, construction industry still accounts for a substantial number of occupational accidents leading to injuries in different body parts of construction workers. In this vein, detecting the most susceptible body parts of construction workers by using only pre-accident information is of particular importance by means of performance augmentation using advanced modelling techniques as it helps safety managers orchestrate the most relevant and adequate mitigation measures. The central focus of this study is to identify the susceptible body parts of construction workers proactively and propose measures specific to the corresponding body parts. Hence, this study aims to develop a machine learning (ML)-based novel inclusive multi-stage ensemble model that identifies the most vulnerable body parts of construction workers using a national dataset recorded in Turkey.

FINDINGS illustrate that incorporating ensemble modelling approach into predictions enhanced accuracies and the ensemble random forest (RF) model reinforced with principal component analysis (PCA) yielded the best performance.

RESULTS further highlight that number of workers in the company, working days of the worker, and age of the worker are the most influential attributes in the susceptibility of body parts. A utilization plan is developed based on the analysis results, which can be run monthly on construction sites to identify the most vulnerable body parts of construction workers. Overall, this study is expected to contribute to the development of more robust safety management applications by allowing safety managers to evaluate susceptible body parts of construction workers prior to accidents.

Language: en

Keywords

Body part susceptibility; Construction safety; Inclusive modelling; Machine learning; Occupational health and safety (OHS)