Inland Waterway Infrastructure Maintenance Prediction Model Based on Network-Level Assessment
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Published:2023-11-16
Issue:22
Volume:15
Page:16027
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ISSN:2071-1050
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Container-title:Sustainability
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language:en
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Short-container-title:Sustainability
Author:
Zhang Fan12, Wang Pingyi1, Liu Huaihan3, Zhang Bin1ORCID, Sun Jianle4, Li Jian1ORCID
Affiliation:
1. Key Laboratory of Hydraulic and Waterway Engineering of Ministry of Education, Chongqing Jiaotong University, Chongqing 400074, China 2. School of Civil Architectural and Engineering, Yangtze Normal University, Chongqing 408100, China 3. Changjiang Waterway Bureau, Wuhan 430010, China 4. Information Center, Yangtze Normal University, Chongqing 408100, China
Abstract
Maintenance decision optimization based on network-level assessment has a long history in road transportation infrastructure and has greatly assisted management departments in saving in expenditure on maintenance costs. However, its application and research in water transportation infrastructure have been lacking. This paper aims to design a predictive model for waterway improvement building maintenance based on network-level assessment and provide a new solution for optimizing the allocation of limited maintenance funds for inland waterway infrastructure. The proposed network-level assessment framework and predictive model comprise data collection, maintenance prediction, and maintenance decision modules. A small time-series dataset was constructed based on the classification proportions of improvement building technical conditions in the jurisdiction of the Yangtze River trunk waterway over the past five years. The two-parameter moving average method was transformed into a single-parameter “jurisdiction moving average method” to suit the characteristics of the dataset. Three models, namely the jurisdiction moving average (JMA), the linear regression (LR), and the quadratic curve regression (QCR) models, were employed to perform calculations on the dataset, which were evaluated using t-tests and error analysis. The research results indicated that both the JMA and LR models showed good overall performance and were recommended for use. Especially, the confidence intervals of the JMA model increased the credibility of the prediction results, making it the ideal choice. This study also found that the inland waterway maintenance prediction technology based on the network-level evaluation has higher overall efficiency than the known existing technologies. The proposed predictive model allows for a simple and rapid assessment of the overall risk status of regional waterway facilities and is easy to promote and apply.
Funder
National Key Research and Development Program of China Natural Science Foundation of Chongqing, China Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, China Research and Innovation Program for Graduate Students in Chongqing, China
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference28 articles.
1. Application of collaborative serious gaming for the elicitation of expert knowledge and towards creating Situation Awareness in the field of infrastructure resilience;Wehrle;Int. J. Disaster Risk Reduct.,2022 2. Jiang, Z., Chu, X., Yu, Z., Guo, J., and Chu, X. (2022, January 18–21). Technical Status Evaluation of River Training Works Based on the Improved DS Evidence Theory. Proceedings of the PIANC Smart Rivers 2022, Green Waterways and Sustainable Navigations, Nanjing, China. Lecture Notes in Civil Engineering. 3. Wang, P., Yang, C., Yu, T., Li, J., Zhang, M., and Fu, Z. (2016). Water Damage Mechanism and Monitoring and Repair Technology of Waterway Improvement Buildings in the Upper Yangtze River, Science Press. 4. Study on the Evaluation of the maintenance effect of the Yangtze River Trunk Waterway Regulation buildings in 2018;Fan;China Water Transp. Sci. Technol. Waterw.,2020 5. Level System for the Resilience Assessment of critical Transport Infrastructures using the Example of Waterways;Wehrle;Bautechnik,2020
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