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Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms
Shock and Vibration ( IF 1.6 ) Pub Date : 2021-12-06 , DOI: 10.1155/2021/8969389
Hanbo Zhu 1, 2 , Changqing Miao 1, 2
Affiliation  

In the fragility analysis, researchers mostly chose and constructed seismic intensity measures (IMs) according to past experience and personal preference, resulting in large dispersion between the sample of engineering demand parameter (EDP) and the regression function with IM as the independent variable. This problem needs to be solved urgently. Firstly, the existing 46 types of ground motion intensity measures were taken as a candidate set, and the composite intensity measures (IMs) based on machine learning methods were selected and constructed. Secondly, the modified Park–Ang damage index was taken as EDP, and the symbolic regression method was used to fit the functional relationship between the composite intensity measures (CIMs) and EDP. Finally, the probabilistic seismic demand analysis (PSDA) and seismic fragility analysis were performed by the cloud-stripe method. Taking the pier of a three-span continuous reinforced concrete hollow slab bridge as an example, a nonlinear finite element model was established for vulnerability analysis. And the composite IM was compared with the linear composite IM constructed by Kiani, Lu Dagang, and Liu Tingting. The functions of them were compared. The analysis results indicated that the standard deviation of the composite IM fragility curve proposed in this paper is 60% to 70% smaller than the other composite indicators which verified the efficiency, practicality, proficiency, and sufficiency of the proposed machine learning and symbolic regression fusion algorithms in constructing composite IMs.

中文翻译:

基于机器学习和符号回归融合算法的钢筋混凝土连续桥墩抗震易损性分析

在脆弱性分析中,研究人员大多根据以往经验和个人喜好选择和构建地震烈度测度(IMs),导致工程需求参数(EDP)样本与以IM为自变量的回归函数之间存在较大的离散度。这个问题急需解决。首先,将现有的46种地震动强度测度作为候选集,选择并构建基于机器学习方法的复合强度测度(IMs)。其次,将修正后的Park-Ang损伤指数作为EDP,采用符号回归方法拟合复合强度测度(CIMs)与EDP之间的函数关系。最后,概率地震需求分析(PSDA)和地震脆性分析采用云带法。以某三跨连续钢筋混凝土空心板桥桥墩为例,建立非线性有限元模型进行易损性分析。并将复合 IM 与由 Kiani、卢大刚和刘婷婷构建的线性复合 IM 进行了比较。对它们的功能进行了比较。分析结果表明,本文提出的复合IM脆性曲线的标准偏差比其他复合指标小60%~70%,验证了所提出的机器学习和符号回归融合的效率、实用性、熟练性和充分性构建复合 IM 的算法。以某三跨连续钢筋混凝土空心板桥桥墩为例,建立非线性有限元模型进行易损性分析。并将复合 IM 与由 Kiani、卢大刚和刘婷婷构建的线性复合 IM 进行了比较。对它们的功能进行了比较。分析结果表明,本文提出的复合IM脆性曲线的标准偏差比其他复合指标小60%~70%,验证了所提出的机器学习和符号回归融合的效率、实用性、熟练性和充分性构建复合 IM 的算法。以某三跨连续钢筋混凝土空心板桥桥墩为例,建立非线性有限元模型进行易损性分析。并将复合 IM 与由 Kiani、卢大刚和刘婷婷构建的线性复合 IM 进行了比较。对它们的功能进行了比较。分析结果表明,本文提出的复合IM脆性曲线的标准偏差比其他复合指标小60%~70%,验证了所提出的机器学习和符号回归融合的效率、实用性、熟练性和充分性构建复合 IM 的算法。并将复合 IM 与由 Kiani、卢大刚和刘婷婷构建的线性复合 IM 进行了比较。对它们的功能进行了比较。分析结果表明,本文提出的复合IM脆性曲线的标准偏差比其他复合指标小60%~70%,验证了所提出的机器学习和符号回归融合的效率、实用性、熟练性和充分性构建复合 IM 的算法。并将复合 IM 与由 Kiani、卢大刚和刘婷婷构建的线性复合 IM 进行了比较。对它们的功能进行了比较。分析结果表明,本文提出的复合IM脆性曲线的标准偏差比其他复合指标小60%~70%,验证了所提出的机器学习和符号回归融合的效率、实用性、熟练性和充分性构建复合 IM 的算法。
更新日期:2021-12-06
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