Predicting Construction Workers’ Intentions to Engage in Unsafe Behaviours Using Machine Learning Algorithms and Taxonomy of Personality
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/12/6/841 |
| _version_ | 1827661641681993728 |
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| author | Yifan Gao Vicente A. González Tak Wing Yiu Guillermo Cabrera-Guerrero Ruiqi Deng |
| author_facet | Yifan Gao Vicente A. González Tak Wing Yiu Guillermo Cabrera-Guerrero Ruiqi Deng |
| author_sort | Yifan Gao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T00:14:03Z |
| format | Article |
| id | doaj.art-6e6d80bbd5e049d785466f32b612c221 |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-10T00:14:03Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-6e6d80bbd5e049d785466f32b612c2212023-11-23T15:54:14ZengMDPI AGBuildings2075-53092022-06-0112684110.3390/buildings12060841Predicting Construction Workers’ Intentions to Engage in Unsafe Behaviours Using Machine Learning Algorithms and Taxonomy of PersonalityYifan Gao0Vicente A. González1Tak Wing Yiu2Guillermo Cabrera-Guerrero3Ruiqi Deng4College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaCivil and Environmental Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, AB T6G 2R3, CanadaSchool of Built Environment, Massey University, Auckland 4472, New ZealandEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2950, ChileDepartment of Educational Technology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou 311121, ChinaDynamic 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.https://www.mdpi.com/2075-5309/12/6/841machine learningpersonality configurationunsafe-behaving intentions |
| spellingShingle | Yifan Gao Vicente A. González Tak Wing Yiu Guillermo Cabrera-Guerrero Ruiqi Deng Predicting Construction Workers’ Intentions to Engage in Unsafe Behaviours Using Machine Learning Algorithms and Taxonomy of Personality Buildings machine learning personality configuration unsafe-behaving intentions |
| title | Predicting Construction Workers’ Intentions to Engage in Unsafe Behaviours Using Machine Learning Algorithms and Taxonomy of Personality |
| title_full | Predicting Construction Workers’ Intentions to Engage in Unsafe Behaviours Using Machine Learning Algorithms and Taxonomy of Personality |
| title_fullStr | Predicting Construction Workers’ Intentions to Engage in Unsafe Behaviours Using Machine Learning Algorithms and Taxonomy of Personality |
| title_full_unstemmed | Predicting Construction Workers’ Intentions to Engage in Unsafe Behaviours Using Machine Learning Algorithms and Taxonomy of Personality |
| title_short | Predicting Construction Workers’ Intentions to Engage in Unsafe Behaviours Using Machine Learning Algorithms and Taxonomy of Personality |
| title_sort | predicting construction workers intentions to engage in unsafe behaviours using machine learning algorithms and taxonomy of personality |
| topic | machine learning personality configuration unsafe-behaving intentions |
| url | https://www.mdpi.com/2075-5309/12/6/841 |
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