发表机构
University of Virginia; University of Illinois at Urbana Champaign; University of Maryland-College Park（弗吉尼亚大学; 伊利诺伊大学厄巴纳-香槟分校; 马里兰大学帕克分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对竣工信息不完整的桥梁，提出基于有限元模型修正（FEMU）结合遗传算法与梯度法的优化算法，经四座不同劣化程度的在用公路桥梁验证，其荷载评定偏差在0-17%，可有效解决相关工程问题。
AI 中文摘要
荷载评定是确定现有桥梁结构安全承载能力的工程过程，通常通过分析其承重构件及其横截面来完成。然而，当结构的设计图纸和细节缺失或不足以进行此类计算时，必须采用替代方案来推断承载能力。本研究提出了一种基于结构识别（St-Id）的合理且一致的承载能力推断工程解决方案。该方法采用有限元模型修正（FEMU）来估算结构的未知特性，用于分析性荷载评定，这需要开发一个初始模型，该模型在ABAQUS中开发，可根据试验测试数据进行修正。ABAQUS允许开发与MATLAB的接口，这促进了自动迭代参数优化算法的集成。本研究开发的优化算法结合了遗传算法（GA）和基于梯度的方案的特征，以对未知参数进行迭代。该方法在四座在用公路桥梁上进行了评估，其中包括两座混凝土板式结构和两座T型梁结构，处于不同的劣化状态，这些桥梁有足够的设计文件，但被视为具有不同程度的未知细节。结果表明，有限元（FE）模型修正方法生成的荷载评定值与目标荷载评定值的偏差在0%至17%之间，同时缓解了现有替代方案的主要挑战。
英文摘要
Load rating is the engineering process of determining the safe load-carrying capacity of an existing bridge structure often through analysis of its load-bearing members and their cross sections. However, when structural drawings and details of the structure are missing or insufficient for such calculations, alternative solutions must be used to infer the capacity. This study proposes a rational and consistent engineering solution for capacity inference based on structural identification (St-Id). The proposed approach uses finite element model updating (FEMU) to estimate the unknown characteristics of structures for use in an analytical load rating, which requires the development of an initial model developed in ABAQUS that can be updated based on experimental test data. ABAQUS allowed for the development of an interface with MATLAB, which facilitated the integration of an automatic iterative parameter optimization algorithm. The optimization algorithm developed in this investigation incorporated the features of a genetic algorithm (GA) and a gradient-based scheme to iterate on the unknown parameters. The proposed approach was evaluated on four in-service highway bridges including two concrete slab and two T-beam structures in varying deterioration condition states, which had sufficient plans available but were treated as having varying degrees of unknown details. The results illustrated that the finite element (FE) model updating approach generated load ratings that were within 0-17% of the target load ratings while alleviating the main challenges of the existing alternatives.
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