**6. Conclusions**

Different sets of drivers underlie different safety behaviors, and uncovering such complex patterns, help formulate targeted measures to cultivate safety behaviors. Machine learning can explore such complex patterns among safety behavioral data. Given the theoretical, methodological and practical significance, this paper attempts to develop a classification framework for construction personnel's safety behaviors with machine-learning algorithms, including LR, SVM, RF, and CatBoost. The classification framework has three steps, i.e., data collection and preprocessing, modeling and algorithm implementation, and optimal model acquisition. For illustrative purposes, five common safety behaviors of a random and representative sample of Hong Kong-based construction personnel are used to validate the classification framework. To achieve a high classification performance, this paper employs a combinative strategy of CatBoost–MOSMA. Results support this combinative strategy in dealing with construction safety behavioral data. From the derived optimal model, a unique set of important features can be identified for each safety behavior, and ten out of the 46 input indicators are found important for all the five safety behaviors. Based on the findings, safety staff is supposed to make concrete and targeted interventions to individual construction personnel on site, and improve safety performance in a more efficient and effective way.

**Author Contributions:** Conceptualization, S.Y. and Y.S.; methodology, S.Y. and Y.W.; validation, Y.S. and Y.W.; formal analysis, Y.S.; investigation, S.Y., Y.W., Y.S. and S.R.; writing—original draft preparation, S.Y., Y.W. and Y.S.; writing—review and editing, Y.S., S.Y. and S.R.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study is supported by a grant from the National Natural Science Foundation of China (Project No.: 71701130).

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of the University of Hong Kong (protocol code EA011011 and 6 October 2011).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
