|Table of Contents|

Degradation characteristics of regional bridges and green low-carbon management and maintenance decision(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2026年01期
Page:
108-117
Research Field:
桥梁与隧道工程
Publishing date:

Info

Title:
Degradation characteristics of regional bridges and green low-carbon management and maintenance decision
Author(s):
ZHANG Wen-wu1;  LI Jiang-feng2;  LONG Guan-xu3;  GAO Xin-kai4;  XIA Ye2*
(1. Shandong Hi-Speed Group Co., Ltd., Jinan 250098, Shandong, China; 2. College of Civil Engineering,Tongji University, Shanghai 200092, China; 3. Innovation Research Institute, Shandong Hi-Speed Group Co., Ltd., Jinan 250098, Shandong, China; 4. Shandong Hi-Speed Engineering Consulting Group Co., Ltd., Jinan 250002, Shandong, China)
Keywords:
bridge engineering;  NSGA-Ⅲ;  deterioration model;  maintenance decision;  green low-carbon
PACS:
U445.7
DOI:
10.19721/j.cnki.1671-8879.2026.01.008
Abstract:
To adapt bridge maintenance to current sustainable development requirements, a management and maintenance decision model integrating the bridge deterioration prediction, cost control, and green low-carbon objectives was constructed. Through the investigation and mechanism analysis, bridge location, design speed, subgrade width, traffic volume, bridge age, and bridge type were selected as the characteristic columns. A bridge deterioration prediction model was developed based on the back propagation(BP)neural network. The overall, superstructure, substructure, and deck conditions of bridges were classified into five grades. A calculation framework for bridge maintenance carbon emissions was proposed, including two parts: maintenance work and traffic delay. Carbon emission factors required for calculation in material production and transportation, energy, and construction machinery phases were systematically collected and organized. The dynamic carbon emission factor was introduced based on the decay of carbon dioxide in the atmosphere over time to reflect the attenuation effect. The calculation method was progressively derived from a single bridge with a single maintenance, to a single bridge with multiple maintenances, and finally to multiple bridges with multiple maintenances. On this basis, a multi-objective optimization model with three objective functions, such as the maintenance cost, safety rating, and carbon dioxide emission, was constructed using the non-dominated sorting genetic algorithm-Ⅲ(NSGA-Ⅲ). Taking the Shandong Expressway Network as an example, 7 225 sets of data from 1 362 bridges were selected for model training and validation. By setting different cost coefficients and adjusting the number of layers in the BP neural network model, the properties of deterioration models with different neural network parameters were tested using accuracy as the evaluation indicator, and the optimal model was selected. This deterioration model was further combined with NSGA-Ⅲ. The Pareto front solution sets were obtained through non-dominated sorting, and by setting different differentiation points, maintenance strategies with different emphases on indicators were derived. The research results show that under the conditions of a 4-layer hidden network structure and adopting a cost coefficient with a higher penalty weight to prediction errors for lower condition grades, the deterioration model achieves a classification accuracy of 76.5%, showing a clear improvement compared to the baseline model with the same network structure but considering only equal error costs(the classification accuracy is 67.2%). This indicates that the adopted cost-sensitive learning strategy can improve the model's prediction property for critical conditions. The Pareto solution set obtained by combining the relatively optimal deterioration prediction model with NSGA-Ⅲ includes the cost-oriented, balanced, safety-oriented, and green-oriented solutions. Each solution cannot improve one indicator without sacrificing others, making them local optimal solutions. While these diverse solutions can meet the varied needs in bridge maintenance.6 tabs, 8 figs, 30 refs.

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Last Update: 2026-02-20