基于刚度分离法的大跨度钢桁梁桥局部模型损伤识别

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Structural Control & Health Monitoring Pub Date : 2024-08-31 DOI:10.1155/2024/5530300
Feng Xiao, Yuxue Mao, Geng Tian, Gang S. Chen
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引用次数: 0

摘要

桥梁结构的损伤检测一直是一项挑战,尤其是对于结构形式复杂的大跨度桥梁。本研究提出了一种基于局部模型的损伤检测方法,用于大跨度钢桁架桥梁的损伤识别。该方法采用部分模型,利用刚度分离法估算参数。这种方法无需为结构构建完整的刚度信息。相反,它完全依赖于结构构件的布置和识别区域的材料信息。这种技术可以有效避免构建整体结构模型,降低大型结构损伤识别的复杂性。我们使用了一座现役的全尺寸大跨度钢桁架桥来说明建议方法的可行性。在模型分析中考虑了三个局部模型的位置,并比较了 Nelder-Mead 单纯形法和准牛顿算法的参数估计效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Partial-Model-Based Damage Identification of Long-Span Steel Truss Bridge Based on Stiffness Separation Method

Damage detection in bridge structures has always been challenging, particularly for long-span bridges with complex structural forms. In this study, a partial-model-based damage detection method was proposed for the damage identification of long-span steel truss bridges. The proposed method employs partial models to estimate the parameters using the stiffness separation method. This approach obviates the need to construct complete stiffness information for the structure. In contrast, it depends solely on the arrangement of the structural members and material information in the recognized area. This technique can effectively circumvent the construction of an overall structural model and reduce the complexity of damage identification in large structures. A full-scale long-span steel truss bridge in service was used to illustrate the feasibility of the proposed method. The locations of the three partial models were considered in the model analysis, and the parameter estimation efficiency of the Nelder–Mead simplex and quasi-Newton algorithms were compared.

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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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