**1. Introduction**

Unsafe behaviors are the primary direct cause of construction accidents. Different types of accidents can be attributed to different sets of unsafe behaviors [1]. For example, to avoid falls from height the management should take care of unprotected holes/borders and correct workers' inappropriate use of personal protective equipment (PPE). Safety behavior is traditionally categorized as either safety compliance or safety participation. The former is an in-role task-related behavior, while the latter involves extra-role behaviors, which are voluntary and initiated by employees [2]. Griffin and Curcuruto further identify two categories of safety participation behavior: affiliative and proactive [3]. Helping and stewardship behaviors, civic virtue, and caring for safety are typical of affiliative safety participation behavior, whereas proactive safety participation behavior includes safety voice behaviors and initiating safety-related changes. Affiliative safety participation behavior is related to minor incidents, such as property damage and microinjuries, while proactive

**Citation:** Yin, S.; Wu, Y.; Shen, Y.; Rowlinson, S. Development of a Classification Framework for Construction Personnel's Safety Behavior Based on Machine Learning. *Buildings* **2023**, *13*, 43. https://doi.org/10.3390/ buildings13010043

Academic Editor: Ahmed Senouci

Received: 24 November 2022 Revised: 19 December 2022 Accepted: 21 December 2022 Published: 24 December 2022

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

safety participation behavior is positively associated with near-miss reporting. Therefore, it can be hypothesized that different sets of drivers are accountable for different (un)safety behaviors. This paper attempts to validate this hypothesis with a machine-learning-enabled classification framework.

Besides the theoretical significance, this paper also has both a practical and a methodological significance as well. On the practical front, if different patterns of drivers for different safety behaviors are ascertained, targeted interventions can be proposed accordingly. Specifically, this paper selects five typical safety behaviors, i.e., the use of all necessary safety equipment to do the job (hereafter coded as SB1); following safety procedures in doing the job (hereafter coded as SB2); promoting safety programs willingly (hereafter coded as SB3); put in extra effort to improve workplace safety (hereafter coded as SB4); and help colleagues out when they are under risky conditions (hereafter coded as SB5). On the methodological front, as a subset of artificial intelligence, machine learning enables a system to learn from example data or past experience without explicit programming. Like traditional statistical modelling, it is also intended to seek solutions from data. Unlike traditional methods that are based on assumptions and ignore the nonlinear relationship among independent variables, machine learning methods are more flexible, have fundamental and simple assumptions, and take into consideration the complex relationship among independent variables. Machine learning has seen an increasing use by safety researchers in recent years. Construction workers' risk perceptions have a direct impact on their safety behavior. The traditional measurement of risk perceptions primarily relies on a post hoc survey-based assessment, which has limitations such as lack of objectivity and continuous monitoring ability. Given this, Lee et al. developed an automatic system to measure workers' risk perception using physiological signals obtained by wristband-type wearable biosensors in combination with a supervised learning algorithm [4]. Overexertion-induced work-related musculoskeletal disorders (WMSDs) are a primary cause of the nonfatal injuries for construction workers. To reduce overexertion, appropriate levels of physical loads need to be identified. In this regard, Yang et al. propose to employ a bidirectional long short-term memory algorithm to classify physical load levels, and investigate the feasibility of such an approach with a laboratory experiment [5]. In view of machine learning's advantage in predictive accuracy, Goh et al. use six supervised learning algorithms (i.e., support vector machine, random forest, K-nearest neighbor, naïve Bayes, artificial neural network, and decision tree) to assess the relative importance of different cognitive factors derived from the theory of reasoned action in affecting safety behavior [6].

Given the theoretical, practical, and methodological significance, a machine-learningenabled safety behavior classification framework should be developed in order to improve construction safety performance in an efficient and effective way. In particular, this paper has two objectives, namely: (a) To identify drivers of different safety behaviors; (b) To propose new machine learning methods in predicting safety behaviors. The former intends to make targeted interventions for different safety behaviors based on the findings and the latter to explore new algorithms which are more suitable for analyzing safety-related behavioral data.

This paper is organized as follows. First, a safety behavior factor analysis and classification system is developed based on the literature review. Second, the sample, measures, machine learning models, and classification outputs are described. Third, results are presented, with an emphasis on model performance and factor importance analysis. Finally, both the contribution and limitations of the findings are discussed along with future research directions.

#### **2. Safety Behavior Factor Analysis and Classification System**

Safety behavior is an emergent property of a more complex system. Choi and Lee find that construction workers' safety behavior is a function of their socio-cognitive process and their interaction with the environment [7]. Based on bibliometric and content analyses of 101 empirical studies, Xia et al. propose a safety behavior antecedent analysis and classification system [8], which organizes the antecedents of safety behavior into five levels: (a) Self; (b) Work; (c) Home; (d) Work–home interface; (e) Industry/society. In addition, they put forward a resource flow model to explain how safety behavior emerges from such a complex system. Using Xia et al. 's framework [8], this study organizes influencing factors of construction safety behavior at four levels, i.e., client, project, group, and individual, and hence, develops a safety behavior factor analysis and classification system as well. The next section deliberates on the impact of these factors on safety behavior before presenting the system.
