AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
Due to the complex and dynamic nature of construction environments, safety accidents occurring in these environments pose a grave threat to life and property. Therefore, it is essential for safety managers in construction, supervisory, and related units to adopt a rigorous and systematic methodology for assessing the risks associated with construction safety accidents. This will enable managers to comprehend the likelihood of accidents, subsequently enabling them to implement preemptive and control measures to reduce the probability of such incidents. Drawing on the accident causation theory, this study utilized web crawler technology to collect construction accident reports, subsequently employing text mining (TM) techniques to identify the accident causal factors specified in 166 accident reports. Subsequently, 33 key features were extracted from the accident causal factors, and correlation rule mining was used to analyze the correlations between the causal factors. Successively, a Bayesian network (BN)-based risk assessment model was constructed for construction safety accidents. Finally, through reverse reasoning, this study identified the probable paths of construction safety accidents and the sensitive factors that trigger such accidents. The results showed that management factors (MFs) are the primary drivers of accidents, highlighting the importance of focusing on preventive and control countermeasures for factors characterized with high severity and sensitivity.
S. L. Tang, K. C. Ying, W. Y. Chan, et al. Impact of social safety investments on social costs of construction accidents. Constr Manage Econ, 2004, 22: 937–946.
H. Abbasianjahromi, M. Aghakarimi. Safety performance prediction and modification strategies for construction projects via machine learning techniques. Eng Constr Archit Manage, 2023, 30: 1146–1164.
H. Yao, C. Y. Chen, D. N. Song. Coupling safety risk assessment of su-per high-rise building construction based on complex network. J Saf Environ, 2021, 21: 957–968 (in Chinese)
B. Q. Sun, X. Yang, S. Wu, et al. Application of Bayesian network in the safety risk assessment of flight test. Flight Dyn, 2019, 37: 92–96. (in Chinese)
S. Y. Guo, J. L. He, J. C. Li, et al. Exploring the impact of unsafe behaviors on building construction accidents using a Bayesian network. Int J Environ Res Public Health, 2020, 17: 221.
D. M. Guo, G. Q. Li, N. L. Hu, et al. Big data analysis and visualization of potential hazardous risks of the mine based on text mining. Chin J Eng, 2022, 44: 328–338. (in Chinese)
B. Y. Chen, J. Ding, S. N. Chen. Selection of key incentives for power production safety accidents based on association rule mining. Electr Power Autom Equip, 2018, 38: 68–74. (in Chinese)
S. Chen, J. B. Xi, J. P. Wang, et al. Mining association rules of near-misses of hydropower projects construction. China Saf Sci J, 2021, 31: 75–82. (in Chinese)
X. M. Du, J. F. Qin, S. Y. Guo, et al. Text mining of typical defects in power equipment. High Voltage Eng, 2018, 44: 1078–1084. (in Chinese)
D. H. Guo, G. Zheng, J. Zhang, et al. Study on laws of TCM diagnosis and treatment of chronic cough based on text mining. Chin J Inf Tradit Chin Med, 2019, 26: 101–104. (in Chinese)
L. Y. Wang, G. Zheng, X. Y. Zhao, et al. Analysis of traditional chinese medicine health care in hypertension based on text mining. Chin J Basic Med Tradit Chin Med, 2018, 24: 199–200,217. (in Chinese)
N. T. Chen, J. H. Li, Y. Z. Man, et al. Risk factors analysis of approach and landing based on civil aviation safety information text mining. J Saf Sci Technol, 2022, 18: 5–10. (in Chinese)
X. M. Ni, H. W. Wang, M. L. Xiong, et al. Civil aviation incident risk assessment based on text mining. J Hunan Univ (Nat Sci), 2022, 49: 73–79. (in Chinese)
J. Li, Y. F. Wang. Causation network analysis of the construction falling or collapsing accidents based on the text mining. J Saf Environ, 2020, 20: 1284–1290. (in Chinese)
M. Cavalcanti, L. Lessa, B. M. Vasconcelos. Construction accident prevention: A systematic review of machine learning approaches. Work, 2023, 76: 507–519.
N. Xu, L. Ma, Q. Liu, et al. An improved text mining approach to extract safety risk factors from construction accident reports. Saf Sci, 2021, 138: 105216.
S. C. Tian, X. C. Wang, B. B. Fan. Research on causes of collapse accidents in building construction based on text mining. J Xi’an Univ Sci Technol, 2022, 42: 849–855. (in Chinese)
M. Kim, J. Lee, J. Kim. Analysis of design change mechanism in apartment housing projects using association rule mining (ARM) model. Appl Sci, 2022, 12: 11036.
N. Xu, B. Zhang, T. T. Gu, et al. Expanding domain knowledge elements for metro construction safety risk management using a co-occurrence-based pathfinding approach. Buildings, 2022, 12: 1510.
A. M. Jones, D. R. Jones. A novel Bayesian general medical diagnostic assistant achieves superior accuracy with sparse history: A performance comparison of 7 online diagnostic aids and physicians. Front Artif Intell, 2022, 5: 727486.
G. Kabir, R. Sadiq, S. Tesfamariam. A fuzzy Bayesian belief network for safety assessment of oil and gas pipelines. Struct Infrastruct Eng, 2016, 12: 874–889.
W. Chen, H. Wang, G. H. Zhang, et al. Evaluation of tunnel collapse susceptibility based on T– S fuzzy fault tree and Bayesian network. J Shanghai Jiaotong Univ, 2020, 54: 820–830. (in Chinese)
B. Jitwasinkul, B. H. W. Hadikusumo, A. Q. Memon. A Bayesian belief network model of organizational factors for improving safe work behaviors in Thai construction industry. Saf Sci, 2016, 82: 264–273.
M. N. Asrar, T. J. W. Adi. Prediction model safety perfomance model on the dam construction project based Bayesian networks. IOP Conf Ser Earth Environ Sci, 2021, 832: 012055.
I. Mohammadfam, F. Ghasemi, O. Kalatpour, et al. Constructing a Bayesian network model for improving safety behavior of employees at workplaces. Appl Ergon, 2017, 58: 35–47.
T. S. Zhao, W. Zhou, K. Xu, et al. Analysis and Bayesian modeling of tower crane safety risk during the use phase. Sci Technol Eng, 2019, 19: 350–356. (in Chinese)
X. Y. Lu, C. S. Xu, B. W. Hou, et al. Risk assessment of metro construction based on dynamic Bayesian network. Chin J Geotech Eng, 2022, 44: 492–501. (in Chinese)
Y. Zhou, C. S. Li, C. Zhou, et al. Using Bayesian network for safety risk analysis of diaphragm wall deflection based on field data. Reliab Eng Syst Saf, 2018, 180: 152–167.
N. N. Xue, J. R. Zhang, W. Zhang, et al. Research on causes of construction safety accidents using Bayesian network. J Civil Eng Manage, 2021, 38: 176–182,194. (in Chinese)
W. J. Li, L. B. Zhang, W. Liang. An accident causation analysis and taxonomy (ACAT) model of complex industrial system from both system safety and control theory perspectives. Saf Sci, 2017, 92: 94–103.
H. Lingard, T. Cooke, G. Zelic, et al. A qualitative analysis of crane safety incident causation in the Australian construction industry. Saf Sci, 2021, 133: 105028.
S. R. Mohandes, H. Sadeghi, A. Fazeli, et al. Causal analysis of accidents on construction sites: A hybrid fuzzy Delphi and DEMATEL approach. Saf Sci, 2022, 151: 105730.
Q. Xu, T. Li, Y. H. Song, et al. Research on the classification method of electricity production potential accident based on three dimensional risk function. Ind Saf Environ Prot, 2017, 43: 36–39,47. (in Chinese)
F. Ghasemi, O. Kalatpour, A. Moghimbeigi, et al. Selecting strategies to reduce high-risk unsafe work behaviors using the safety behavior sampling technique and Bayesian network analysis. J Res Health Sci, 2017, 17: e00372.
M. Mohajeri, A. Ardeshir, M. T. Banki. Using association rules to investigate causality patterns of safety-related incidents in the construction industry. Sci Iran, 2022, 29: 929–939.
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
京公网安备11010802044758号