Development of a Classification Framework for Construction Personnel’s Safety Behavior Based on Machine Learning
Different sets of drivers underlie different safety behaviors, and uncovering such complex patterns helps formulate targeted measures to cultivate safety behaviors. Machine learning can explore such complex patterns among safety behavioral data. This paper aims to develop a classification framework...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/13/1/43 |
| _version_ | 1797445137031233536 |
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| author | Shiyi Yin Yaoping Wu Yuzhong Shen Steve Rowlinson |
| author_facet | Shiyi Yin Yaoping Wu Yuzhong Shen Steve Rowlinson |
| author_sort | Shiyi Yin |
| collection | DOAJ |
| description | Different sets of drivers underlie different safety behaviors, and uncovering such complex patterns helps formulate targeted measures to cultivate safety behaviors. Machine learning can explore such complex patterns among safety behavioral data. This paper aims to develop a classification framework for construction personnel’s safety behaviors with machine learning algorithms, including logistics regression (LR), support vector machine (SVM), random forest (RF), and categorical boosting (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 sample of Hong Kong-based construction personnel are used to validate the classification framework. To achieve high classification performance, this paper employed a combinative strategy, consisting of feature selection, synthetic minority over-sampling technique (SMOTE), one-hot encoding, standard scaler and classifiers to classify safety behaviors, and multi-objective slime mould algorithm (MOSMA) to optimize parameters in the classifiers. Results suggest that the combinative strategy of CatBoost–MOSMA achieves the highest classification performance with the maximum average scores, including area under the curve of receiver characteristic operator (AUC) ranging from 0.84 to 0.92, accuracy ranging from 0.80 to 0.86, and F1-score ranging from 0.79 to 0.86. From the optimal model, a unique set of important features was identified for each safety behavior, and ten out of the 46 input indicators were found important for all five safety behaviors. Based on the findings, this study advocates using the machine learning strategy of CatBoost–MOSMA in future construction safety behavior research and makes concrete and targeted suggestions to cultivate different construction safety behaviors. |
| first_indexed | 2024-03-09T13:21:23Z |
| format | Article |
| id | doaj.art-69f4b6473b42479cbe49f979f2f5f264 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-09T13:21:23Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-69f4b6473b42479cbe49f979f2f5f2642023-11-30T21:29:05ZengMDPI AGBuildings2075-53092022-12-011314310.3390/buildings13010043Development of a Classification Framework for Construction Personnel’s Safety Behavior Based on Machine LearningShiyi Yin0Yaoping Wu1Yuzhong Shen2Steve Rowlinson3College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, ChinaCollege of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, ChinaCollege of Civil Engineering, Shanghai Normal University, Shanghai 201418, ChinaDepartment of Real Estate and Construction, University of Hong Kong, Hong Kong, ChinaDifferent sets of drivers underlie different safety behaviors, and uncovering such complex patterns helps formulate targeted measures to cultivate safety behaviors. Machine learning can explore such complex patterns among safety behavioral data. This paper aims to develop a classification framework for construction personnel’s safety behaviors with machine learning algorithms, including logistics regression (LR), support vector machine (SVM), random forest (RF), and categorical boosting (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 sample of Hong Kong-based construction personnel are used to validate the classification framework. To achieve high classification performance, this paper employed a combinative strategy, consisting of feature selection, synthetic minority over-sampling technique (SMOTE), one-hot encoding, standard scaler and classifiers to classify safety behaviors, and multi-objective slime mould algorithm (MOSMA) to optimize parameters in the classifiers. Results suggest that the combinative strategy of CatBoost–MOSMA achieves the highest classification performance with the maximum average scores, including area under the curve of receiver characteristic operator (AUC) ranging from 0.84 to 0.92, accuracy ranging from 0.80 to 0.86, and F1-score ranging from 0.79 to 0.86. From the optimal model, a unique set of important features was identified for each safety behavior, and ten out of the 46 input indicators were found important for all five safety behaviors. Based on the findings, this study advocates using the machine learning strategy of CatBoost–MOSMA in future construction safety behavior research and makes concrete and targeted suggestions to cultivate different construction safety behaviors.https://www.mdpi.com/2075-5309/13/1/43classificationsafety behaviorconstruction personnelmachine learningMOSMA |
| spellingShingle | Shiyi Yin Yaoping Wu Yuzhong Shen Steve Rowlinson Development of a Classification Framework for Construction Personnel’s Safety Behavior Based on Machine Learning Buildings classification safety behavior construction personnel machine learning MOSMA |
| title | Development of a Classification Framework for Construction Personnel’s Safety Behavior Based on Machine Learning |
| title_full | Development of a Classification Framework for Construction Personnel’s Safety Behavior Based on Machine Learning |
| title_fullStr | Development of a Classification Framework for Construction Personnel’s Safety Behavior Based on Machine Learning |
| title_full_unstemmed | Development of a Classification Framework for Construction Personnel’s Safety Behavior Based on Machine Learning |
| title_short | Development of a Classification Framework for Construction Personnel’s Safety Behavior Based on Machine Learning |
| title_sort | development of a classification framework for construction personnel s safety behavior based on machine learning |
| topic | classification safety behavior construction personnel machine learning MOSMA |
| url | https://www.mdpi.com/2075-5309/13/1/43 |
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