基于时序卷积神经网络的场地地震效应模拟  

Temporal convolution neural network model for simulation of site seismic effect

作　　者：胡晓虎 陈苏 金立国 傅磊[1] 王苏阳 刘献伟 Hu Xiaohu;Chen Su;Jin Liguo;Fu Lei;Wang Suyang;Liu Xianwei(Institute of Geophysics,China Earthquake Administration,Beijing 100081,China;Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]中国地震局地球物理研究所,100081北京 [2]北京工业大学城市与工程安全减灾教育部重点实验室,北京100124

出　　处：《地震学报》2024年第5期893-905,共13页Acta Seismologica Sinica

基　　金：国家自然科学基金重大项目(52192675);地震科学联合基金(U1839202)共同资助

摘　　要：场地地震效应模拟作为岩土地震工程学的热点与难点,多基于数学物理方法或观测记录开展研究,需面对动力方程求解、建模不确定性、数据稀疏、泛化能力等问题。针对以上问题,本文构建了物理嵌入的时序卷积神经网络(Phy-TCN)模型,并验证了其与纯数据驱动的时序卷积网络(TCN)的性能差异。针对KiK-net数据库中场地井上/井下强震记录,采用Phy-TCN模型开展了场地地震效应模拟。结果表明:Phy-TCN模型可有效模拟时序型数据;在KiK-net观测记录等含噪信号模拟中,以选取站点的地震事件特定周期点反应谱值为基准,Phy-TCN模型和等效线性化方法所得数据与实测记录的平均相对误差分别为0.067和0.379。基于上述结果认为,Phy-TCN模型可应用于土层剖面信息模糊条件的场地地震效应模拟。The simulation of site seismic effects is a critical and challenging research area in earthquake engineering,providing a scientific basis for seismic safety evaluations of engineering sites,seismic fortification of buildings,and code revisions.Four primary research paradigms are typically employed:the empirical research paradigm,which relies on earthquake damage data;the theoretical research paradigm,which uses model experiments and mathematical tools to describe experimental phenomena;the computational research paradigm,which employs numerical methods to solve complex physical problems;and the data-driven paradigm,which utilizes machine learning tools to identify patterns in large datasets.Despite these approaches,challenges such as sparse data samples,weak generalization of results,and insufficient understanding of underlying laws persist.In this study,we introduce a fifth research paradigm,artificial intelligence for science,represented by physics-embedded deep learning.We investigate site seismic effects using strong motion records from the Japanese KiK-net array on-site/borehole stations.In this study,we primarily employ temporal convolution neural network(TCN)as the deep learning framework.Compared with traditional recurrent neural networks(RNNs,LSTMs,GRUs),TCN offers stronger parallelism and more flexible receptive fields.TCN uses a onedimensional fully convolutional network architecture,with dilated causal convolutions to exponentially increase the receptive field,thus avoiding the loss of historical information when processing long sequences.Additionally,TCN uses residual blocks to prevent gradient vanishing issues.We detail how to impose physical constraints on the loss function of deep learning neural networks and develop a physics-embedded temporal convolution neural network(Phy-TCN)model.To validate the effectiveness of the Phy-TCN model,we generated a simple sparse sample dataset.Specifically,we used 30 sets of random white noise sequences with length 1000 as excitations for a single degree of freed

关 键 词：时序卷积网络 场地地震效应 数据驱动 物理嵌入 深度学习 

分 类 号：TU435[建筑科学—岩土工程] P315.9[建筑科学—土工工程]