Bridge Damage Identification Using Artificial Neural Networks
Bridge Damage Identification Using Artificial Neural Networks
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DOI：
10.1061/(asce)be.1943-5592.0001302
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发表时间：
2018-11
影响因子：
3.6
通讯作者：
Jordan C. Weinstein;M. Sanayei;B. Brenner
中科院分区：
文献类型：
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作者：
Jordan C. Weinstein;M. Sanayei;B. Brenner
介绍了一种客观的，数据驱动的方法来评估桥梁的性能，开发一个结构健康监测系统的桥梁行为。本文提出了一种通过对桥梁振动响应数据的评估来识别结构损伤的方法。在马萨诸塞州巴雷的粉磨桥的运营交通事件期间，在桥上的许多位置记录了应变。桥梁行为被定义为每个传感器位置在交通事件期间的预期峰值应变的范围，该范围基于在该时刻测量的所有其他传感器位置的应变。人工神经网络（ANN）的训练与操作的桥梁响应数据在一个自举方案，以产生一个概率模型的桥梁行为。当对新的数据进行测试时，预测桥梁行为的人工神经网络学习模型被证明是有效的，并适用于未知负载条件下的不同交通事件。提出了一种基于桥梁预期行为的长期性能评估方法。结构损伤会影响桥梁行为，从而影响桥梁性能。结构损伤的影响提取模拟HS 20设计卡车运行校准的有限元模型（FEM），并施加到操作应变数据，以评估损伤识别方法。当评估时，损伤识别方法在检测损伤的存在方面是有效的，当使用适当显著性水平的Wilcoxon秩和检验时，没有I型或II型错误。对于大多数类型的模拟损伤，损伤都能有效定位。DOI：10.1061/（ASCE）BE.1943-5592.0001302。© 2018美国土木工程师学会。作者关键词：结构健康监测人工神经网络;桥梁行为;损伤识别;工作应变测量;仅响应;假设检验。
An objective, data-driven approach to evaluate the performance of bridges for developing a structural health monitoring system is introduced as bridge behavior. A method of identifying structural damage through the evaluation of response data from an instrumented bridge is proposed. Strains during operational traffic events at the Powder Mill Bridge in Barre, Massachusetts, are recorded at many locations on the bridge. Bridge behavior is defined as each sensor location’s range of expected peak strain during a traffic event based on all other sensor locations’ strains measured at that instance in time. Artificial neural networks (ANNs) are trained with operational bridge response data in a bootstrapping scheme to generate a probabilistic model of bridge behavior. When tested against new data, the ANN-learned model of predicted bridge behavior is proven effective and applicable to varying traffic events with unknown loading conditions. A method for long-term performance assessment using the expected bridge behavior is proposed. Structural damage can impact bridge behavior and thus bridge performance. The effects of structural damage are extracted from simulated HS20 design truck runs on a calibrated finite-element model (FEM) and are applied to operational strain data to assess the damage identification method. When assessed, the damage identification method is effective at detecting the presence of damage, with no Type I or Type II errors when using aWilcoxon rank-sum test of an appropriate significance level. Damage is effectively localized for most types of simulated damage.DOI: 10.1061/(ASCE)BE.1943-5592.0001302.© 2018 American Society of Civil Engineers. Author keywords: Structural health monitoring; Artificial neural networks (ANNs); Bridge behavior; Damage identification; Operational strain measurements; Response only; Hypothesis test.