基于有限元预训练潜在动力学的稀疏观测地震场地响应预测

中文摘要 AI 辅助

数值场地响应预测常常偏离观测结果，然而由于记录在传感器覆盖范围和事件数量上均有限，纠正这些偏差十分困难。本研究提出了用于响应方程的迁移启用强制潜在自编码器（FLARE-T），通过学习并校准连接基底加速度输入与多个深度处加速度输出的低维潜在动力学来改进这些预测。FLARE-T从密集的有限元模拟中学习低维响应流形和输入驱动的动力学。随后，它训练一个稀疏编码器将模拟的传感器响应映射到所学坐标中，并利用有限的记录来校准其中的动力学。一个短响应窗口用于初始化每次预测，而完整的基底运动则驱动响应。该框架使用分层土离心机试验和Lotung现场竖向阵列进行了评估。测试集结果表明，相对于原始有限元模型，FLARE-T改进了多深度加速度时程和5%阻尼伪加速度响应谱，在评估的每个传感器上针对不同强度的运动均减少了误差，并且在Lotung场地对两个水平分量均如此。两个具有不同本构参数的Lotung源模型实现了相当的测试集精度，表明对精确先验校准的依赖性降低。因此，FLARE-T提供了一种数据高效的方法，将密集的数值响应信息与有限的现场记录相结合，以改进未来的场地响应预测。

英文摘要

Numerical site-response predictions often deviate from observations, yet correcting these discrepancies is difficult because records are limited in both sensor coverage and number of events. This study proposes the Transfer-Enabled Forced Latent Autoencoder for Response Equations (FLARE-T) to improve these predictions by learning and calibrating low-dimensional latent dynamics that connect the base acceleration input to acceleration outputs at multiple depths. FLARE-T learns a low-dimensional response manifold and input-driven dynamics from dense finite-element simulations. It then trains a sparse encoder to map simulated sensor responses into the learned coordinates and uses limited records to calibrate the dynamics within them. A short response window initializes each prediction, while the complete base motion drives the response. The framework was evaluated using a layered-soil centrifuge test and the Lotung field vertical array. Test-set results show that FLARE-T improved multi-depth acceleration histories and 5%-damped pseudoacceleration response spectra relative to the original finite-element models, reducing errors at every evaluated sensor for motions of different intensities and, at Lotung, for both horizontal components. Two Lotung source models with different constitutive parameters achieved comparable test-set accuracy, indicating reduced dependence on precise prior calibration. FLARE-T therefore provides a data-efficient means of combining dense numerical response information with limited field records to improve future site-response predictions.

发表机构

• Beijing University of Technology（北京工业大学）

机构由 AI 辅助整理，请以论文原文为准。