Inland Waterway Infrastructure Maintenance Prediction Model Based on Network-Level Assessment

Fan Zhang, Pingyi Wang (), Huaihan Liu, Bin Zhang, Jianle Sun and Jian Li
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Fan Zhang: Key Laboratory of Hydraulic and Waterway Engineering of Ministry of Education, Chongqing Jiaotong University, Chongqing 400074, China
Pingyi Wang: Key Laboratory of Hydraulic and Waterway Engineering of Ministry of Education, Chongqing Jiaotong University, Chongqing 400074, China
Huaihan Liu: Changjiang Waterway Bureau, Wuhan 430010, China
Bin Zhang: Key Laboratory of Hydraulic and Waterway Engineering of Ministry of Education, Chongqing Jiaotong University, Chongqing 400074, China
Jianle Sun: Information Center, Yangtze Normal University, Chongqing 408100, China
Jian Li: Key Laboratory of Hydraulic and Waterway Engineering of Ministry of Education, Chongqing Jiaotong University, Chongqing 400074, China

Sustainability, 2023, vol. 15, issue 22, 1-17

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.

Keywords: inland waterway; improvement building; network-level assessment; prediction; moving average method; regression analysis; maintenance decision optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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