用于 KiK 网井下阵列场地土壤地震响应建模的多输入集成神经网络

IF 4.3 2区 工程技术 Q1 ENGINEERING, CIVIL
Lin Li, Feng Jin, Duruo Huang, Gang Wang
{"title":"用于 KiK 网井下阵列场地土壤地震响应建模的多输入集成神经网络","authors":"Lin Li,&nbsp;Feng Jin,&nbsp;Duruo Huang,&nbsp;Gang Wang","doi":"10.1002/eqe.4155","DOIUrl":null,"url":null,"abstract":"<p>Prediction of the soil seismic response is of primary importance for geotechnical earthquake engineering. Conventional physics-based models such as the finite element method (FEM) often face challenges due to inherent model assumptions and uncertainties of model parameters. Furthermore, these physics-based models require significant computational resources, particularly when simulating seismic responses across numerous soil sites. In this study, a multi- input integrative neural network is developed for predicting soil seismic response based on the recorded data from a large number of downhole array sites. Ground motions, seismic event information, and wave velocity structures of the sites are utilized as input data in the proposed neural network, enabling the model to adapt to various site conditions. Comparative assessments against state-of-the-art FEM models demonstrate that the proposed models exhibit superior prediction performance with increased efficiency. Furthermore, the pre-training technique, a transfer learning method, is employed to predict the seismic response at new stations. By fine-tuning the pre-trained model derived from the extensive dataset with limited recorded data from new stations, high-precision seismic response predictions can be realized, illustrating the adaptability and efficacy of the proposed approach in data-scarce conditions.</p>","PeriodicalId":11390,"journal":{"name":"Earthquake Engineering & Structural Dynamics","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-input integrative neural network for soil seismic response modeling at KiK-net downhole array sites\",\"authors\":\"Lin Li,&nbsp;Feng Jin,&nbsp;Duruo Huang,&nbsp;Gang Wang\",\"doi\":\"10.1002/eqe.4155\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Prediction of the soil seismic response is of primary importance for geotechnical earthquake engineering. Conventional physics-based models such as the finite element method (FEM) often face challenges due to inherent model assumptions and uncertainties of model parameters. Furthermore, these physics-based models require significant computational resources, particularly when simulating seismic responses across numerous soil sites. In this study, a multi- input integrative neural network is developed for predicting soil seismic response based on the recorded data from a large number of downhole array sites. Ground motions, seismic event information, and wave velocity structures of the sites are utilized as input data in the proposed neural network, enabling the model to adapt to various site conditions. Comparative assessments against state-of-the-art FEM models demonstrate that the proposed models exhibit superior prediction performance with increased efficiency. Furthermore, the pre-training technique, a transfer learning method, is employed to predict the seismic response at new stations. By fine-tuning the pre-trained model derived from the extensive dataset with limited recorded data from new stations, high-precision seismic response predictions can be realized, illustrating the adaptability and efficacy of the proposed approach in data-scarce conditions.</p>\",\"PeriodicalId\":11390,\"journal\":{\"name\":\"Earthquake Engineering & Structural Dynamics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earthquake Engineering & Structural Dynamics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/eqe.4155\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earthquake Engineering & Structural Dynamics","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/eqe.4155","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0

摘要

土壤地震响应预测对于岩土地震工程至关重要。由于固有的模型假设和模型参数的不确定性,有限元法(FEM)等传统的物理模型经常面临挑战。此外,这些基于物理的模型需要大量的计算资源,尤其是在模拟众多土壤场地的地震响应时。本研究开发了一种多输入集成神经网络,用于根据大量井下阵列站点的记录数据预测土壤地震响应。地面运动、地震事件信息和场地的波速结构都被用作神经网络的输入数据,使模型能够适应各种场地条件。与最先进的有限元模型进行的比较评估表明,所提出的模型在提高效率的同时,还表现出了卓越的预测性能。此外,预训练技术是一种迁移学习方法,可用于预测新台站的地震响应。通过利用新台站的有限记录数据对从大量数据集中得出的预训练模型进行微调,可以实现高精度的地震反应预测,这说明了所提方法在数据稀缺条件下的适应性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-input integrative neural network for soil seismic response modeling at KiK-net downhole array sites

Prediction of the soil seismic response is of primary importance for geotechnical earthquake engineering. Conventional physics-based models such as the finite element method (FEM) often face challenges due to inherent model assumptions and uncertainties of model parameters. Furthermore, these physics-based models require significant computational resources, particularly when simulating seismic responses across numerous soil sites. In this study, a multi- input integrative neural network is developed for predicting soil seismic response based on the recorded data from a large number of downhole array sites. Ground motions, seismic event information, and wave velocity structures of the sites are utilized as input data in the proposed neural network, enabling the model to adapt to various site conditions. Comparative assessments against state-of-the-art FEM models demonstrate that the proposed models exhibit superior prediction performance with increased efficiency. Furthermore, the pre-training technique, a transfer learning method, is employed to predict the seismic response at new stations. By fine-tuning the pre-trained model derived from the extensive dataset with limited recorded data from new stations, high-precision seismic response predictions can be realized, illustrating the adaptability and efficacy of the proposed approach in data-scarce conditions.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Earthquake Engineering & Structural Dynamics
Earthquake Engineering & Structural Dynamics 工程技术-工程:地质
CiteScore
7.20
自引率
13.30%
发文量
180
审稿时长
4.8 months
期刊介绍: Earthquake Engineering and Structural Dynamics provides a forum for the publication of papers on several aspects of engineering related to earthquakes. The problems in this field, and their solutions, are international in character and require knowledge of several traditional disciplines; the Journal will reflect this. Papers that may be relevant but do not emphasize earthquake engineering and related structural dynamics are not suitable for the Journal. Relevant topics include the following: ground motions for analysis and design geotechnical earthquake engineering probabilistic and deterministic methods of dynamic analysis experimental behaviour of structures seismic protective systems system identification risk assessment seismic code requirements methods for earthquake-resistant design and retrofit of structures.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信