基于高斯过程回归的桥梁多变量地震易损性分析

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摘要本文建立了一种基于高斯过程回归的多变量地震易损性分析方法，可以将易损性表达为由地震动强度指标和结构参数组成的矢量输入的多元函数，并以此为基础发展了均值易损性曲线与包络易损性曲线，能够定量分析所有不确定参数对易损性的影响。以一座三跨钢筋混凝土连续箱梁桥为例进行了分析与验证，结果表明所提出的方法能够以数百次的动力分析达到与蒙特卡罗法(数十万次动力分析)相一致的精度；并识别了不同的地震强度指标对易损性的影响，发现任何单一的参数均会造成易损性的不精确(离散)呈现；对于本文算例，结果显示平均谱加速度是一个用来生成最小离散度的均值与包络易损性曲线的最优指标。
关键词：多变量地震易损性分析；均值易损性曲线；包络易损性曲线；高斯过程回归；钢筋混凝土连续梁桥

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	闫业祥1
	孙利民1
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关键词 ：多变量地震易损性分析, 均值易损性曲线, 包络易损性曲线, 高斯过程回归, 钢筋混凝土连续梁桥

Abstract：This paper establishes a multivariate seismic fragility analysis method based on Gaussian process regression, which presents fragility as a multivariate function of vector-valued input composed of ground motion intensity indicators and structural parameters. Based on this, the mean fragility curve and envelope fragility curve are developed, quantitatively analyzing the impact of all uncertain parameters on fragility. A three-span reinforced concrete continuous box girder bridge is used as an example to analyze and verify. The results show that the proposed method is comparatively satisfactory in accuracy compared with the Monte Carlo simulation (by hundreds of thousands of dynamic analyses) with hundreds of dynamic analyses. The impact of different seismic intensity indicators on fragility is identified, and the fact is found that any single parameter will cause inaccurate and discrete presentation of fragility. For the case in this paper, the average spectral acceleration is an optimal index to generate the mean and envelope fragility curve with the minimum dispersion.
Key words: multivariate seismic fragility analysis; mean fragility curve; envelope fragility curve; Gaussian process regression; reinforced concrete continuous girder bridge.

Key words： multivariate seismic fragility analysis mean fragility curve envelope fragility curve Gaussian process regression reinforced concrete continuous girder bridge.

收稿日期: 2021-08-30 出版日期: 2022-12-15

引用本文:

闫业祥1，孙利民1,2,3. 基于高斯过程回归的桥梁多变量地震易损性分析[J]. 振动与冲击, 2022, 41(23): 27-35.
YAN Yexiang1, SUN Limin1,2,3. Multivariate seismic vulnerability analysis of bridges based on Gaussian process regression. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(23): 27-35.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I23/27

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