首页|A Quantitative Seismic Topographic Effect Prediction Method Based upon BP Neural Network Algorithm and FEM Simulation

A Quantitative Seismic Topographic Effect Prediction Method Based upon BP Neural Network Algorithm and FEM Simulation

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Topography can strongly affect ground motion,and studies of the quantification of hill surfaces'topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method based upon the BP neural network algorithm and three-dimensional finite ele-ment method(FEM)was developed.The FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing ele-vation,but the correlation between PGA amplification factors and slope is not obvious for low hills.New BP neural network models were established for the prediction of amplification factors of PGA and response spectra.Two kinds of input variables'combinations which are convenient to achieve are pro-posed in this paper for the prediction of amplification factors of PGA and response spectra,respective-ly.The absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors,and they can be mostly within 0.2 for response spectra's amplification factors.One input variables'combi-nation can achieve better prediction performance while the other one has better expandability of the predictive region.Particularly,the BP models only employ one hidden layer with about a hundred nodes,which makes it efficient for training.

seismic topographic effectfinite element methodBP neural network algorithmearth-quake disaster prevention

Qifeng Jiang、Mianshui Rong、Wei Wei、Tingting Chen

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Shandong Seismic Hazard Prevention Center,Shandong Earthquake Agency,Jinan 250014,China

Shandong Institute of Earthquake Engineering,Jinan 250021,China

Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education,Beijing University of Technology,Beijing 100124,China

Publicity and Education Center,Shandong Earthquake Agency,Jinan 250014,China

Shandong Earthquake Station,Shandong Earthquake Agency,Jinan 250014,China

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National Natural Science Foundation of ChinaCollaboratory for the Study of Earthquake Predictability in China Seismic Experimental SiteGeneral Scientific Research Foundation of Shandong Earthquake Agency

518786252018YFE0109700YB2208

2024

10.1007/s12583-022-1795-x

地球科学学刊(英文版)
中国地质大学

地球科学学刊(英文版)

CSTPCD
影响因子：0.724
ISSN：1674-487X
年,卷(期)：2024.35(4)
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