Two-stage stochastic model updating method for highway bridges based on long-gauge strain sensing
Two-stage stochastic model updating method for highway bridges based on long-gauge strain sensing
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DOI：
10.1016/j.istruc.2022.01.082
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发表时间：
2022-02-02
期刊：
影响因子：
4.1
通讯作者：
Wu，Gang
中科院分区：
文献类型：
--
作者：
Chen，Shi-Zhi;Zhong，Qiang-Ming;Wu，Gang
目前，公路桥梁的总数正在迅速增长。为了保证桥梁的安全，对桥梁进行准确的评估是必要的。在现有的方法中，通常需要一个能反映桥梁实际情况的有限元模型。因此，桥梁模型的修正是不可避免的。虽然已经提出了许多模型修正方法，但仍然存在一些局限性，例如难以从测量数据中获取有效的结构信息以及需要耗时的优化模拟。在此背景下，为了提高桥梁模型修正的效率和精度，基于新的长应变时程，提出了一种基于径向基函数（RBF）神经网络和贝叶斯理论的桥梁模型修正方法。通过一系列算例初步验证了该方法的可行性。通过室内模型试验进一步研究了该方法的性能。结果表明，该方法在各种条件下都能取得较好的效果，具有实际应用的潜力。
Currently, the total number of highway bridges is growing rapidly. To ensure the safety, accurate evaluation of bridges is necessary. Among the existing methods, a finite element model which can reflects the bridge’s actual condition is usually required. Thus, the bridge model updating is inevitable. Although many model updating methods have been proposed, there are still some limitations, such as the difficulty in acquisition of effective structural information from measured data and the need for time-consuming optimization simulations. Under these backgrounds, based on novel long-gauge strain time history, the study proposes a two-stage bridge model updating method by combining a radial basis function (RBF) neural network with Bayesian theory to increase its efficiency and accuracy on highway bridges. This method’s feasibility was tentatively verified through a series of numerical cases. An indoor model experiment was also conducted to further investigate this method’s performance. The results demonstrated that this method performs well under various conditions and has the potential to be applied in actual cases.