| 引用本文: | 刘巴黎,胡进军,谢礼立.基于弹性网络回归的地震动参数排序与比选[J].哈尔滨工业大学学报,2024,56(1):54.DOI:10.11918/202302060 |
| LIU Bali,HU Jinjun,XIE Lili.Ranking and comparison of ground motion parameters based on elastic net regression[J].Journal of Harbin Institute of Technology,2024,56(1):54.DOI:10.11918/202302060 |
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| 摘要: |
| 为合理选取地震动参数以有效减小结构损伤预测的不确定性,基于弹性网络回归技术对多个地震动参数进行了比选。基于多种地震动参数以及大量单自由度（SDOF）模型的增量动力分析结果建立弹性网络回归模型,定义回归模型中回归系数值以及回归系数值为非零值的个数分别为地震动参数的敏感性系数和频数。基于地震动参数敏感性和频数分析结果对多种地震动参数进行排序,研究结构恢复力模型、地震动集和结构极限状态对地震动参数排序结果的影响。基于一座8层钢框架结构分析结果验证了基于弹性网络回归的地震动参数比选方法的有效性。结果表明:选取代表性的地震动参数加入最小二乘回归模型时,不同结构极限状态下回归分析中残差标准差显著减小。基于大量SDOF体系的地震动参数排序结果比选出了受地震动集、结构恢复力模型和结构极限状态影响较小的地震动参数。结果可为结构抗震能力预测用地震动参数的比选提供理论基础。 |
| 关键词: 地震动参数 弹性网络回归 抗震能力 增量动力分析 单自由度体系 |
| DOI：10.11918/202302060 |
| 分类号:P315.9 |
| 文献标识码:A |
| 基金项目:国家自然科学基金重点项目(U1939210) |
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| Ranking and comparison of ground motion parameters based on elastic net regression |
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LIU Bali1,2,HU Jinjun1,2,XIE Lili1,2
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(1.Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China; 2.Key Lab of Earthquake Engineering and Engineering Vibration of China Earthquake Administration (Institute of Engineering Mechanics, China Earthquake Administration), Harbin 150080, China)
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| Abstract: |
| To reasonably select a suitable set of ground motion parameters and effectively reduce the uncertainty of structural damage prediction, various ground motion parameters were preferentially selected based on the elastic network regression technique. First, the elastic network regression model was established based on various ground motion parameters and the seismic capacity of a generic set of single-degree of freedom (SDOF) systems obtained from the results of incremental dynamical analysis. Second, the values of regression coefficients in the elastic network regression model and the number of times that the regression coefficients have non-zero values were used to define the sensitivity and frequency of ground motion parameters, respectively. Third, the ranking of ground motion parameters used for seismic capacity prediction was established in terms of sensitivity and frequency of ground motion parameters obtained from the results of elastic network regression on a generic set of SDOF systems. Results were statistically organized to evaluate the influence of various ground motions, structural types and structural limit-states. The analysis result obtained from an 8-story steel frame verified that the use of ground motion parameters selected based on elastic network regression can effectively reduce the uncertainty of structural damage prediction. In addition, results showed that the standard deviation of the residuals in the regression analysis for different structural limit-states was significantly reduced when the representative ground motion parameters were employed in the least squares regression model. Moreover, representative ground motion parameters that are less affected by various ground motions, structural types and structural limit-states were selected based on the ranking results obtained from a generic set of SDOF systems. Findings of this study can provide a theoretical basis for the comparison of ground motion parameters used for the prediction of structural seismic capacity. |
| Key words: ground motion parameter elastic net regression seismic capacity incremental dynamic analysis single-degree of freedom system |
