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

Abstract

Dynamic environmental circumstances can sometimes be incompatible with proactive human intentions of being safe, leading individuals to take unintended risks. Behaviour predictions, as performed in previous studies, are found to involve environmental circumstances as predictors, which might thereby result in biased safety conclusions about individuals’ inner intentions to engage in unsafe behaviours. This research calls attention to relatively less-understood worker intentions and provides a machine learning (ML) approach to help understand workers’ intentions to engage in unsafe behaviours based on the workers’ inner drives, i.e., personality. Personality is consistent across circumstances and allows insight into one’s intentions. To mathematically develop the approach, data on personality and behavioural intentions was collected from 268 workers. Five ML architectures—backpropagation neural network (BP-NN), decision tree, support vector machine, k-nearest neighbours, and multivariate linear regression—were used to capture the predictive relationship. The results showed that BP-NN outperformed other algorithms, yielding minimal prediction loss, and was determined to be the best approach. The approach can generate quantifiable predictions to understand the extent of workers’ inner intentions to engage in unsafe behaviours. Such knowledge is useful for understanding undesirable aspects in different workers in order to recommend suitable preventive strategies for workers with different needs.

Individual Characteristics as Enablers of Construction Employees’ Digital Literacy: An Exploration of Leaders’ Opinions

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

“…In risk management field 25 out of 61 papers dealt with data on the "risk management process". Into this class the 54 observations out of 202 concerned the following topics: awkward working postures [7,19]; compliance with Health and Safety standards [2,4,47]; risk assessment [21,51,53,63,66,77,79]; safe climate [44]; slope instability [10,17]; teaching-training tasks [5,6,78]; unsafe behaviours [25,72]; worker fatigue-heat stress [38,69,73]; site image [1,46]. Antwi-Afari et al [7] used deep learning networks to automatically extract relevant features with spatial-temporal dependence acquired by a wearable insole pressure system.…”