Enhancing Hazard Detection and Risk Severity Assessment in Construction through Multinomial Naive Bayes and Regression

Authors

  • Akaninyene Michael Akwaisua Akwa Ibom State University, Nigeria
  • Anietie Ekong Akwa Ibom State University, Nigeria
  • Godwin Ansa Akwa Ibom State University, Nigeria
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DOI:

https://doi.org/10.51519/journalisi.v7i1.979

Keywords:

Hazard, Risk, Construction, Regression Analysis, Naive Bayes, Machine Learning.

Abstract

This research delves into the crucial area of hazard detection and risk severity assessment within the construction industry, using machine learning techniques. The dataset utilized is from the Chinese Construction Company (CCECC), Uyo, Nigeria. Comprising over 100,000 instances, it captures various hazard categories prevalent in construction sites, providing a comprehensive foundation for predictive analysis. In the first phase of the study, the system is designed to detect hazards present in construction sites. Leveraging these data, the machine learning models are trained to predict potential hazards based on the information provided. Through TF-IDF vectorization, a feature extraction technique, the textual data is transformed into numerical representations. Multinomial Naive Bayes is employed for hazard classification due to its efficacy in handling text data, and with it, an accuracy of 0.99 was obtained. Subsequently, the trained model was evaluated to assess its performance and the severity of identified hazards are evaluated. The system quantifies the potential risk posed by each hazard using the risk severity attribute. Using the Linear Regression algorithm, the model predicts the severity of risks based on textual descriptions of a hazard.  In practical application, the research stresses the significance of risk management strategies in the construction industry to mitigate potential harm to personnel and infrastructure. This research contributes to advancing safety protocols within the construction sector, advocating for a culture of vigilance and precaution to address risks effectively.

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References

V. S. K. Delhi, R. Sankarlal, and A. Thomas, "Detection of personal protective equipment (PPE) compliance on construction site using computer vision-based deep learning techniques," Front. Built Environ., vol. 6, p. 136, 2020.

A. Ekong, H. Odikwa, and O. Ekong, "Minimizing symptom-based diagnostic errors using weighted input variables and fuzzy logic rules in clinical decision support systems," Int. J. Adv. Trends Comput. Sci. Eng., vol. 10, no. 3, pp. 1567–1575, 2021.

A. Ekong, B. Ekong, and A. Edet, "Supervised machine learning model for effective classification of patients with COVID-19 symptoms based on Bayesian belief network," Res. J. Sci. Technol., vol. 2, no. 2, pp. 27–33, 2022.

J. Dumaru, S. Karki, R. Shrestha, and S. S. Khadka, "A review on machine learning approaches for Himalayan rock mass classification," J. Phys. Conf. Ser., vol. 2629, p. 012016, 2023, doi: 10.1088/1742-6596/2629/1/012016.

S. Ahmed, M. A. Hossain, M. M. I. Bhuiyan, and S. K. Ray, "A comparative study of machine learning algorithms to predict road accident severity," in Proc. 20th Int. Conf. Ubiquitous Comput. Commun. (IUCC/CIT/DSCI/SmartCNS), London, U.K., 2021, pp. 390–397.

C. Liao, E. Aminudin, S. Mohd, and S. S. Yap, "Intelligent risk management in construction projects: Systematic literature review," IEEE Access, vol. 10, pp. 72936–72954, 2022.

J. Lee and S. Lee, "Construction site safety management: A computer vision and deep learning approach," Sensors, vol. 23, p. 944, 2023, doi: 10.3390/s23020944.

N. Udoetor, G. Ansa, A. Ekong, and A. Edet, "Intelligent system for detection of copyright-protected data for enhanced data security," Br. J. Comput. Netw. Inf. Technol. (BJCNIT), vol. 7, no. 4, pp. 58–80, 2024.

M. Alateeq and M. Ali, "Construction site hazards identification using deep learning and computer vision," Sustainability, vol. 15, p. 2358, 2023, doi: 10.3390/su15032358.

A. Edet and G. Ansa, "Machine learning enabled system for intelligent classification of host-based intrusion severity," Glob. J. Eng. Technol. Adv., vol. 16, no. 3, pp. 041–050, 2023.

Z. Wei et al., "Insights into the application of machine learning in industrial risk assessment: A bibliometric mapping analysis," Sustainability, vol. 15, p. 6965, 2023, doi: 10.3390/su15086965.

M. Al-Ghzawi and K. El-Rayes, "Machine learning for predicting the impact of construction activities on air traffic operations during airport expansion projects," Autom. Constr., vol. 158, p. 105189, 2024, doi: 10.1016/j.autcon.2023.105189.

M. R. George, M. R. Nalluri, and K. B. Anand, "Application of ensemble machine learning for construction safety risk assessment," J. Inst. Eng. (India): Ser. A, vol. 103, pp. 989–1003, 2022, doi: 10.1007/s40030-022-00690-w.

S. Yin, T. Liu, and B. Huang, "Research on security assessment model of marine information system based on machine learning," in Proc. 2023 Int. Conf. Marine Equip. Technol. Sustainable Dev. (METSD 2023), D. Yang, Ed., 2023, pp. 375–386, doi: 10.1007/978-981-99-4291-6_11.

B. A. Almahameed and M. Bisharah, "Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management," Asian J. Civil Eng., vol. 17, no. 1, pp. 1–15, 2023, doi: 10.1007/s42107-023-00843-7.

S. Elhishi, A. M. Elashry, and S. El-Metwally, "Unboxing machine learning models for concrete strength prediction using XAI," Sci. Rep., vol. 13, p. 19892, 2023, doi: 10.1038/s41598-023-47169-7.

B. T. Pham, C. Vuong, and K. N. Ngo, "Damage assessment in beam-like structures by correlation of spectrum using machine learning," Frattura Integr. Strutt., vol. 17, no. 65, pp. 300–319, 2023, doi: 10.3221/IGF-ESIS.65.20.

O. Lialiuk and R. Osypenko, "Features of the implementation of artificial intelligence in construction," J. Mod. Technol. Mater. Des. Constr., vol. 35, no. 2, pp. 172–176, 2023.

F. Mostofi and V. Toğan, "Explainable safety risk management in construction with unsupervised learning," in Artificial Intelligence and Machine Learning Techniques for Civil Engineering, V. Plevris, A. Ahmad, and N. Lagaros, Eds., IGI Global, 2023, pp. 273–305, doi: 10.4018/978-1-6684-5643-9.ch011.

A. Le Duc et al., "A machine learning approach to risk assessment of expressway bridges," Tạp Chí Khoa Học Giao Thông Vận Tải, vol. 73, no. 7, pp. 661–673, 2023, doi: 10.47869/tcsj.73.7.1.

S. Sarkar et al., "Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data," Safety Sci., vol. 125, p. 104616, 2020, doi: 10.1016/j.ssci.2020.104616.

S. Ballal, K. A. Patel, and D. A. Patel, "Enhancing construction site safety: Natural language processing for hazard identification and prevention," J. Eng. Project Prod. Manag., vol. 14, no. 2, p. 004, 2024.

B. Ekong et al., "Machine learning approach for classification of sickle cell anemia in teenagers based on Bayesian network," J. Inf. Syst. Inform., vol. 5, no. 4, pp. 1793–1808, 2023.

A. Ekong, "Evaluation of machine learning techniques towards early detection of cardiovascular diseases," Am. J. Artif. Intell., vol. 7, no. 1, pp. 6–16, 2023, doi: 10.11648/j.ajai.20230701.12.

A. P. Ekong, G. G. James, and I. Ohaeri, "Oil and gas pipeline leakage detection using IoT and deep learning algorithm," J. Inf. Syst. Inform., vol. 6, no. 1, pp. 421–434, 2024, doi: 10.51519/journalisi.v6i1.652.

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Published

2025-03-18

Issue

Vol. 7 No. 1 (2025): March

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How to Cite

[1]
A. M. Akwaisua, A. Ekong, and G. Ansa, “Enhancing Hazard Detection and Risk Severity Assessment in Construction through Multinomial Naive Bayes and Regression”, journalisi, vol. 7, no. 1, pp. 97–121, Mar. 2025, doi: 10.51519/journalisi.v7i1.979.

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