A machine learning approach to risk assessment of expressway bridges
Email:
daoduylam@utc.edu.vn
Từ khóa:
risk assessment, expressway bridge, machine learning, ANN, Random Forest
Tóm tắt
The expressway network in Vietnam is developing strongly, playing the role of the backbone of the national road system, in which bridge construction accounts for a large proportion. With many specific characteristics and complex risks always hidden in all stages of the expressway project, risk assessment to have solutions and plans to prevent and respond to risks, limiting the impacts of quality assurance and operational safety of the works is essential. However, the current risk assessment and forecasting models still have many limitations. The application of Machine Learning to all aspects of life is getting more popular. This article develops the algorithms, models and program to assess the technical risks in the period of construction and service of expressway bridges in Vietnam using Machine Learning, in order to solve the current limitations in this work. The selection of key influencing factors is especially important in the field of risk assessment. It improves the classification model's performance by focusing only on the most important factors in the data. Via the applications of artificial neural networks and the Random Forest Algorithm in data processing, the performance risks for bridge management can be analyzed, and performed in more detail and exactly. The possible multiple and non-linear relationships of the risks can be investigated. Based on the results, the proposed model helps the managers to make optimum decisions on managing the risks in advance and to obtain sustainable solutionsTài liệu tham khảo
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[2]. Alberto Deco, Dan M. Frangopol, Risk assessment of highway bridges under multiple hazards, Journal of Risk Research, 14 (2011) 1057-1089. https://doi.org/10.1080/13669877.2011.571789
[3]. D. D. Lam, L. D. Anh, N. T. Dong, Report of scientific and technological research project of Ministry of Transport "Research on identification and management of technical risks of expressway bridges", 2019. (in Vietnamese)
[4]. A. Clark-Ginsberg, L. Abolhassani, E. Azam Rahmati, Comparing networked and linear risk assessments: From theory to evidence, International Journal of Disaster Risk Reduction, 30B (2018) 216-224. https://doi.org/10.1016/j.ijdrr.2018.04.031
[5]. L. D. Anh, D. D. Lam, N. T. Dong, Identification of technical risks for bridges on the highway in Vietnam, The Transport Journal, 12 (2019) 40-44. (in Vietnamese)
[6]. J. M. Andric, D. Lu, Risk assessment of bridges under multiple hazards in operation period, Safety Science, 83 (2016) 80-92. https://doi.org/10.1016/j.ssci.2015.11.001
[7]. E. Zio, The future of risk assessment, Reliability Engineering & System Safety, 177 (2018) 176-190. https://doi.org/10.1016/j.ress.2018.04.020
[8]. Z. Chen, Z. Li, C. Huang, G. Zhang, H. Yu, Safety Assessment Method of Bridge Crane Based on Cluster Analysis and Neural Network, Procedia Computer Science, 131 (2018) 477-484. http://dx.doi.org/10.1016/j.procs.2018.04.235
[9]. D. D. Lam, L. D. Anh, N. T. Dong, Risk management of expressway bridge projects in Vietnam: current status and future researches, Proceedings of the Tenth International Conference on Bridge Maintenance, Safety and Management (IABMAS 2020), 1st edition (2021) 1962-1965. https://doi.org/10.1201/9780429279119
[10]. N. V. Trung, D.C. Tam, Risk analysis and management in bridge construction, Civil Construction Public house, 2011. (in Vietnamese)
[11]. N. V. Chau, Researching and managing technical risks in road construction in Vietnam, PhD thesis, University of Transport and Communications, 2016. (in Vietnamese)
[12]. D. D. Lam, N. V. Trung, N. Q. Huy, Solution pour améliorer la maintenance des ponts. Academic Journal of Civil Engineering, 36 (2019) 370-373. https://doi.org/10.26168/ajce.36.1.89
[13]. M. W. Gardner, S. R. Dorling, Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences, Atmospheric Environment, 32 (1998) 2627-2636. https://doi.org/10.1016/S1352-2310(97)00447-0
[14]. H. Jia, J. Lin, J. Liu, An earthquake fatalities assessment method based on feature importance with deep learning and random forest models, Sustainability, 11 (2019) 2727-2748. https://doi.org/10.3390/su11102727
[15]. K. Kira, L. A. Rendell, A practical approach to feature selection. Machine learning proceedings, 1992 (1992) 249-256. https://doi.org/10.1016/B978-1-55860-247-2.50037-1
[16]. T. Shaikhina, D. Lowe, D. Daga, D. Briggs, R. Higgins, N. Khovanova, Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation, Biomedical Signal Processing and Control, 52 (2019) 456-462. https://doi.org/10.1016/j.bspc.2017.01.012
[17]. C. V. Gonzalez Zelaya, Towards explaining the effects of data preprocessing on machine learning, 2019 IEEE 35th international conference on data engineering (ICDE), 2019, pp. 2086-2090. https://doi.org/10.1109/ICDE.2019.00245
[18]. UK ORR, Annual assessment of highways England’s performance, 2018.
[19]. S. Mangalathu, G. Heo, J. S. Jeon, Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes, Engineering Structures, 162 (2018) 166-176. https://doi.org/10.1016/j.engstruct.2018.01.053
[20]. Directorate for Roads of Vietnam, Data from bridge management system, http://www.vbms.vn.
[21]. W. M. P. Van der Aalst, V. Rubin, H. M. W. Verbeek, B. F. Van Dongen, E. Kindler, C. W. Gunther, Process mining: a two-step approach to balance between underfitting and overfitting, Software & Systems Modeling, 9 (2010) 87-111. https://doi.org/10.1007/s10270-008-0106-z
[22]. W. Chen, A. Shirzadi, H. Shahabi, B. B. Ahmad, S. Zhang, H. Hong, N. Zhang, A novel hybrid artificial intelligence approach based on the rotation forest ensemble and native Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China, Geomatics, Natural Hazards and Risk, 8 (2017) 1955-1977. https://doi.org/10.1080/19475705.2017.1401560
[23]. W. Chen, Z. Sun, J. Han, Landslide susceptibility modeling using integrated ensemble weights of evidence with logistic regression and random forest models, Applied Sciences, 9 (2019) 171-197. https://doi.org/10.3390/app9010171
[24]. D. M. Hawkins, The problem of overfitting, Journal of Chemical Information and Computer Sciences, 44 (2004) 1-12. https://doi.org/10.1021/ci0342472
[25]. International Organization for Standardization, ISO 31000:2018, Risk management – Guidelines.
Tải xuống
Chưa có dữ liệu thống kê
Nhận bài
21/03/2022
Nhận bài sửa
29/05/2022
Chấp nhận đăng
31/05/2022
Xuất bản
15/09/2022
Chuyên mục
Công trình khoa học
Kiểu trích dẫn
Le Duc, A., Dao Duy, L., Thai Thi Kim, C., & Bach Thi Diep, P. (1663174800). A machine learning approach to risk assessment of expressway bridges. Tạp Chí Khoa Học Giao Thông Vận Tải, 73(7), 661-673. https://doi.org/10.47869/tcsj.73.7.1
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