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首页> 外文期刊>Journal of Performance of Constructed Facilities >Prediction of Long-Term Bridge Performance: Integrated Deterioration Approach with Case Studies
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Prediction of Long-Term Bridge Performance: Integrated Deterioration Approach with Case Studies

机译：长期桥梁性能的预测：综合恶化方法与案例研究

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摘要

A bridge-deterioration approach is to predict the condition ratings and the deterioration pattern of bridge elements for determining optimal maintenance strategies and estimating future funding requirements. To effectively predict long-term bridge performance, an advanced integrated deterioration approach has been developed that incorporates a time-based model, a state-based model with the Elman neural network (ENN) and a backward prediction model (BPM). The proposed approach involves the categorization of the selected inspection records by bridge components, material types, traffic volume, and the construction era. The primary advantage of such categorization is to group similar components together, thereby identifying the common deterioration patterns. A selection process embedded in the proposed approach offers the ability to automatically select the most appropriate model for predicting future bridge condition ratings. To demonstrate the advantage of the proposed approach in predicting long-term bridge performances, case studies are performed using available inspection records. To compare the performance of the proposed approach against the standard Markovian-based deterioration procedure in predicting future bridge condition ratings, a total of 40 bridges with 464 bridge substructure inspection records are selected and used as input. The predicted outcomes are validated by a cross-validation process, which demonstrates that the proposed approach is more accurate than the standard Markovian-based procedure.
机译：桥梁退化的方法是预测桥梁元件的状况等级和退化模式，以确定最佳的维护策略并估算未来的资金需求。为了有效地预测桥梁的长期性能，已经开发了一种先进的综合劣化方法，该方法结合了基于时间的模型，具有Elman神经网络的基于状态的模型（ENN）和向后预测模型（BPM）。提议的方法涉及按桥梁部件，材料类型，交通量和施工时代对选定的检查记录进行分类。这种分类的主要优点是将相似的组件组合在一起，从而确定常见的劣化模式。提议的方法中嵌入的选择过程提供了自动选择最合适的模型以预测未来桥梁状况等级的能力。为了证明所提出的方法在预测桥梁长期性能方面的优势，使用可用的检查记录进行了案例研究。为了将建议的方法与基于标准马尔科夫的劣化程序的性能在预测未来桥梁状况等级时的性能进行比较，共选择了40座桥梁和464座桥梁子结构检查记录作为输入。预测结果通过交叉验证过程进行验证，这表明所提出的方法比基于标准马尔可夫方法的方法更为准确。

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