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发表期刊：Journal of Bridge Engineering

分区：厂颁滨2区

发表时间：2024年12月6日

第一作者：王天羽

中文摘要：近年来，气候变化引发的自然灾害愈发频繁且严重，对全球交通系统的安全性构成重大威胁。为增强桥梁抵御自然灾害的能力，本文提出了一种基于机器学习（惭尝）的新养护策略，专门用于管理法国境内跨水道桥梁。通过在法国和日本桥梁上测试随机森林（搁贵）和极端梯度提升（齿骋叠辞辞蝉迟）两种机器学习模型，验证了其实际应用价值与稳健性。这些桥梁数据虽从未被模型接触过，但其数据范围与原始数据集高度吻合。为验证未见数据集的预测效果，将法国案例预测结果与工程判断进行比对，发现高级工程师与齿骋叠辞辞蝉迟模型的预测准确率高达95%。而日本案例测试结果与日本指南评分表（厂罢）对比显示，预测精度不及法国案例——这可能源于两国数据分布差异及日本指南中高冲刷风险阈值较低所致。基于原始数据集与未见数据集的分析结果，针对不同模型提出了应用场景建议。最后，为提升模型应用便捷性，开发了用户友好的网页应用程序以降低计算复杂度。本文的研究成果能以智能化的方式有效识别易受冲刷破坏的桥梁，从而最终保障铁路网络的安全。此外，该研究还能为其他国家交通部门制定基于机器学习的维护政策提供参考依据。

英文摘要：Recently, more frequent and severe natural hazards that are caused by climate change have posed a great threat to the safety of transport systems worldwide. To enhance bridges’ resilience to natural hazards, this paper proposes a new maintenance policy that is based on machine learning (ML) for managing bridges that cross waterways in France. Two ML models, for example, random forest (RF) and extreme gradient boosting (XGBoost) classifiers, are tested on bridges in France and Japan to investigate the model’s practicality and robustness.Data from these bridges has never been seen by the model before; however, it is in the same range as the original data set. To verify the test results on the unseen data, predictions from the French cases are compared with engineering judgment, and they are in agreement (95% between the senior engineer and the XGBoost model). When comparing the Japanese case test results with the Japanese guideline’s scoring table (ST),predictions are not as accurate as in the French cases. This might be caused by the different data distribution between the two countries and the lower threshold for high scour risk cases in the Japanese guidelines. Based on the results of the original and unseen data sets, application scenarios are suggested for each model. Finally, to facilitate the use of the proposed model, a friendly web application was demonstrated to reduce computational complexity. The outcome of this paper could help to identify bridges that are vulnerable to scour in an effective yet intelligent way,

which will, in the end, ensure the safety of the rail network. In addition, it could provide insights to other countries’ transport agencies who want to develop their ML-based maintenance polic.