Time-Varying Identification Model for Crack Monitoring Data from Concrete Dams Based on Support Vector Regression and the Bayesian Framework
Time-Varying Identification Model for Crack Monitoring Data from Concrete Dams Based on Support Vector Regression and the Bayesian Framework
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DOI：
10.1155/2017/5450297
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发表时间：
2017-02
影响因子：
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通讯作者：
Bo Chen;Zhongru Wu;J. Liang;Yanhong Dou
中科院分区：
文献类型：
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作者：
Bo Chen;Zhongru Wu;J. Liang;Yanhong Dou
裂缝建模和坝体行为变化识别是大坝健康监测研究中的难点问题。本文利用支持向量回归（SVR）和贝叶斯证据框架（BEF）建立了裂缝监测数据的时变识别模型。首先，采用SVR方法对裂纹张开位移（COD）与其影响因素之间的非线性关系进行了较好的建模；其次，在监测值与模型预测值之间的预测误差尽可能小的原则下，应用BEF方法确定最优SVR建模参数，包括惩罚系数、损失系数和径向核函数的宽度系数。然后，综合考虑预测COD值、历史最大COD值和时间依赖分量，提出了裂缝时变行为和大坝健康异常程度的预警准则。最后，以陈村混凝土拱坝实际结构裂缝的两个监测子序列为例进行了建模和预警分析。研究结果表明，所提出的时变模型能够提供更精确的非线性拟合预测结果，适合用于评价大坝裂缝的行为。
The modeling of cracks and identification of dam behavior changes are difficult issues in dam health monitoring research. In this paper, a time-varying identification model for crack monitoring data is built using support vector regression (SVR) and the Bayesian evidence framework (BEF). First, the SVR method is adopted for better modeling of the nonlinear relationship between the crack opening displacement (COD) and its influencing factors. Second, the BEF approach is applied to determine the optimal SVR modeling parameters, including the penalty coefficient, the loss coefficient, and the width coefficient of the radial kernel function, under the principle that the prediction errors between the monitored and the model forecasted values are as small as possible. Then, considering the predicted COD, the historical maximum COD, and the time-dependent component, forewarning criteria are proposed for identifying the time-varying behavior of cracks and the degree of abnormality of dam health. Finally, an example of modeling and forewarning analysis is presented using two monitoring subsequences from a real structural crack in the Chencun concrete arch-gravity dam. The findings indicate that the proposed time-varying model can provide predicted results that are more accurately nonlinearity fitted and is suitable for use in evaluating the behavior of cracks in dams.