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[口头报告]Rapid seismic response prediction model of bridges with small-sample data based on cluster and multi-level feature fusion deep learning algorithms

Rapid seismic response prediction model of bridges with small-sample data based on cluster and multi-level feature fusion deep learning algorithms
编号:24 访问权限:仅限参会人 更新:2025-07-28 20:13:07 浏览:321次 口头报告

报告开始:2025年08月15日 19:50 (Asia/Shanghai)

报告时间:10min

所在会议:[S1] 8月15日晚上 研究生分会 » [S1-2] 研究生分会场二

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摘要
Under earthquake actions, bridge structures may suffer from various forms of damage, which threaten the overall safety of the structure. Traditional finite element methods have high computational costs in nonlinear time-history analysis. In order to rapidly and accurately assess the seismic performance of bridges, a method combined with cluster and multi-level feature fusion deep learning algorithms with low computational cost and high computational accuracy is proposed. To address the challenge of small-sample data, the DTW-Kmedoids time-series clustering framework is proposed to cluster ground motion records and generate the representative small-sample training set for DL model training. The proposed Multi-level feature fusion GRU model with strong generalization capability and high robustness was trained in small-sample scenarios and is capable of accurately and efficiently predicting the nonlinear response of bridge structures under seismic actions. The effectiveness of the proposed method is verified by comparing the computational results with the traditional finite element model. This study provides a novel and efficient solution for seismic response prediction in the small-sample data scenario of bridge engineering.
 
关键字
Bridge engineering;Multi-level feature fusion mechanism;Clustering strategy;Small-sample data;Seismic response prediction
报告人
卢春德
博士研究生 华中科技大学

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