Predicting Construction Workers’ Intentions to Engage in Unsafe Behaviours Using Machine Learning Algorithms and Taxonomy of Personality

A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management

Increasingly, information technology facilitates the storage and management of data useful for risk analysis and event prediction. Studies on data extraction related to occupational health and safety are increasingly available; however, due to its variability, the construction sector warrants special attention. This review is conducted under the research programs of the National Institute for Occupational Accident Insurance (Inail).

OBJECTIVES

The research question focuses on identifying which data mining (DM) methods, among supervised, unsupervised, and others, are most appropriate for certain investigation objectives, types, and sources of data, as defined by the authors.

METHODS

Scopus and ProQuest were the main sources from which we extracted studies in the field of construction, published between 2014 and 2023. The eligibility criteria applied in the selection of studies were based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). For exploratory purposes, we applied hierarchical clustering, while for in-depth analysis, we used principal component analysis (PCA) and meta-analysis.

RESULTS

The search strategy based on the PRISMA eligibility criteria provided us with 63 out of 2234 potential articles, 206 observations, 89 methodologies, 4 survey purposes, 3 data sources, 7 data types, and 3 resource types. Cluster analysis and PCA organized the information included in the paper dataset into two dimensions and labels: "supervised methods, institutional dataset, and predictive and classificatory purposes" (correlation 0.97-8.18 × 10-1; p-value 7.67 × 10-55-1.28 × 10-22) and the second, Dim2 "not-supervised methods; project, simulation, literature, text data; monitoring, decision-making processes; machinery and environment" (corr. 0.84-0.47; p-value 5.79 × 10-25--3.59 × 10-6). We answered the research question regarding which method, among supervised, unsupervised, or other, is most suitable for application to data in the construction industry.

CONCLUSIONS

The meta-analysis provided an overall estimate of the better effectiveness of supervised methods (Odds Ratio = 0.71, Confidence Interval 0.53-0.96) compared to not-supervised methods.

A critical review of the feasibility of emerging technologies for improving safety behavior on construction sites

INTRODUCTION

Advancements in the modern construction industry have contributed to the development of a range of technology-based interventions to improve the safety behavior of front-line construction workers. Notwithstanding the extensive research on safety behavior, there is still a paucity of research on assessing technology interventions of safety behavior to provide an overview of their strengths and limitations. The present study aims to bridge this gap in the literature and identify the main trends of research.

METHOD

A systematic review and critical content analysis are adopted to capture an overview of the state of knowledge on safety behavior technologies. As a result of searching Scopus, Web of Science, and Google Scholar databases in the period from Jan 2010 to Feb 2023, a total of 359 potential studies went through the systematic screening process and finally, 48 representative studies were selected followed by an assessment of the feasibility and applicability of the safety behavior technologies.

RESULTS

It was found that safety behavior technology is characterized by seven technologies including virtual-reality simulation (T1), eye-tracking technology (T2), prediction modeling of safety behavior (T3), computer-based training (T4), drone/sensor-based hazard monitoring (T5), vision-based behavior monitoring (T6), and real-time positioning (T7).

CONCLUSIONS

This research improves understanding of the status of safety behavior technologies and provides a critical review of their feasibility from the perspective of four assessment criteria including application, limitation, benefit, and feasibility.

PRACTICAL APPLICATIONS

The categorizations of technologies add value to the body of knowledge in terms of generic requirements for their implementation and adaptation on construction sites.