Abstract

Bridge censored databases can be used to analyze and assess structural deterioration conditions, but conducting the analysis is difficult. This difficulty occurs because many factors affect deterioration, and the time span of the data for these factors depends on the years in service of the respective bridge. In addition, the values of some factors are not regularly observed. The present study uses the long short-term memory (LSTM) to consider twelve potentially influencing factors to recognize the relationships between these factors and deterioration grades. Testing the model on an inspection database of 3,368 bridges indicates that the LSTM model obtained an accuracy of exceeding 80%, i.e., outperforms the performance of a multilayer perceptron model established using the same database. For four types of bridges, the LSTM model shows equivalent performance. In addition, the predictive ability of the LSTM model for coastal bridges is slightly superior to non-coastal bridges. No significant differences in accuracy are determined between different deck areas. Practically, the model can predict bridge deterioration paths, and could help decision-makers formulate predictive intervention strategies for improving the quality of maintenance management.

摘要

桥梁审查数据库可用于分析和评估结构恶化情况，但进行分析很困难。出现这种困难是因为影响恶化的因素很多，而这些因素的数据时间跨度取决于相应桥梁的服役年限。此外，一些因素的值没有定期观察。本研究使用长短期记忆 (LSTM) 来考虑十二个潜在影响因素，以识别这些因素与恶化等级之间的关系。在包含 3,368 座桥梁的检测数据库上对该模型进行测试表明，LSTM 模型获得了超过 80% 的准确率，即优于使用相同数据库建立的多层感知器模型的性能。对于四种类型的桥梁，LSTM 模型显示出相同的性能。此外，LSTM 模型对沿海桥梁的预测能力略优于非沿海桥梁。在不同的甲板区域之间没有确定准确度的显着差异。实际上，该模型可以预测桥梁的劣化路径，并可以帮助决策者制定预测性干预策略，以提高维护管理质量。