Enhancing construction safety management through multivariable grey model analysis and variable selection optimization
In this study, a multivariable grey model (GM(1, N)) is employed to explore how different combinations of variables impact the accuracy of construction accident prediction, using a full permutation algorithm. The aim is to optimize variable selection and improve prediction accuracy. By conducting a...
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| Format: | Article |
| Language: | English |
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Vilnius Gediminas Technical University
2025-08-01
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| Series: | Journal of Civil Engineering and Management |
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| Online Access: | https://journals.vilniustech.lt/index.php/JCEM/article/view/24348 |
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| author | Jian Liu Ye He Rui Feng Qinlin Chu |
| author_facet | Jian Liu Ye He Rui Feng Qinlin Chu |
| author_sort | Jian Liu |
| collection | DOAJ |
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In this study, a multivariable grey model (GM(1, N)) is employed to explore how different combinations of variables impact the accuracy of construction accident prediction, using a full permutation algorithm. The aim is to optimize variable selection and improve prediction accuracy. By conducting an exhaustive analysis of 511 potential combinations involving nine variables, it was observed that by integrating crucial external variables such as macroeconomic indicators and industry scale, the multivariable model achieved a prediction accuracy error rate of less than 0.5%, thereby significantly enhancing its information capture and forecasting precision. The analysis suggests that optimal predictive performance is achieved when the number of control variables is approximately four. Additionally, further research shows that increasing the dataset size significantly enhances the model’s predictive capability. This study highlights the scientific rigor and precision of decisionmaking in preventing construction accidents and provides empirical evidence for construction safety management. The research in this paper not only enriches the connotation of the grey system prediction model theoretically, but also provides a data-driven decision support tool for urban construction and safety accident prevention in practice.
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| format | Article |
| id | doaj-art-ac842982b7394b59a535a7eed3a983f0 |
| institution | Kabale University |
| issn | 1392-3730 1822-3605 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Journal of Civil Engineering and Management |
| spelling | doaj-art-ac842982b7394b59a535a7eed3a983f02025-08-24T07:52:56ZengVilnius Gediminas Technical UniversityJournal of Civil Engineering and Management1392-37301822-36052025-08-0131610.3846/jcem.2025.24348Enhancing construction safety management through multivariable grey model analysis and variable selection optimizationJian Liu0Ye He1Rui Feng2https://orcid.org/0000-0003-0425-2611Qinlin Chu3School of Resources and Safety Engineering, University of Science and Technology Beijing, 100083 Beijing, People’s Republic of China; Key Laboratory of High-Efficient Mining and Safety of Metal Mines of the Ministry of Education, University of Science and Technology Beijing, 100083 Beijing, People’s Republic of ChinaChina Institute of Atomic Energy, 103413 Beijing, People’s Republic of ChinaSchool of Resources and Safety Engineering, University of Science and Technology Beijing, 100083 Beijing, People’s Republic of China; Research Institute of Macro-Safety Science, University of Science and Technology Beijing, 100083 Beijing, People’s Republic of ChinaSchool of Resources and Safety Engineering, University of Science and Technology Beijing, 100083 Beijing, People’s Republic of China In this study, a multivariable grey model (GM(1, N)) is employed to explore how different combinations of variables impact the accuracy of construction accident prediction, using a full permutation algorithm. The aim is to optimize variable selection and improve prediction accuracy. By conducting an exhaustive analysis of 511 potential combinations involving nine variables, it was observed that by integrating crucial external variables such as macroeconomic indicators and industry scale, the multivariable model achieved a prediction accuracy error rate of less than 0.5%, thereby significantly enhancing its information capture and forecasting precision. The analysis suggests that optimal predictive performance is achieved when the number of control variables is approximately four. Additionally, further research shows that increasing the dataset size significantly enhances the model’s predictive capability. This study highlights the scientific rigor and precision of decisionmaking in preventing construction accidents and provides empirical evidence for construction safety management. The research in this paper not only enriches the connotation of the grey system prediction model theoretically, but also provides a data-driven decision support tool for urban construction and safety accident prevention in practice. https://journals.vilniustech.lt/index.php/JCEM/article/view/24348multivariable grey modelconstruction safety managementvariable selection optimizationprediction accuracydata size effect |
| spellingShingle | Jian Liu Ye He Rui Feng Qinlin Chu Enhancing construction safety management through multivariable grey model analysis and variable selection optimization Journal of Civil Engineering and Management multivariable grey model construction safety management variable selection optimization prediction accuracy data size effect |
| title | Enhancing construction safety management through multivariable grey model analysis and variable selection optimization |
| title_full | Enhancing construction safety management through multivariable grey model analysis and variable selection optimization |
| title_fullStr | Enhancing construction safety management through multivariable grey model analysis and variable selection optimization |
| title_full_unstemmed | Enhancing construction safety management through multivariable grey model analysis and variable selection optimization |
| title_short | Enhancing construction safety management through multivariable grey model analysis and variable selection optimization |
| title_sort | enhancing construction safety management through multivariable grey model analysis and variable selection optimization |
| topic | multivariable grey model construction safety management variable selection optimization prediction accuracy data size effect |
| url | https://journals.vilniustech.lt/index.php/JCEM/article/view/24348 |
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