Prediction for underground seismic intensity measures using conditional generative adversarial networks
文献类型：期刊论文
| 作者 | Duan, Shuqian1; Song, Zebin2,3; Shen, Jiaxu4,5; Xiong, Jiecheng1 |
| 刊名 | SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 180页码:14 |
| 关键词 | Underground seismic motion Generative adversarial network Seismic intensity measures prediction Deep learning |
| ISSN号 | 0267-7261 |
| DOI | 10.1016/j.soildyn.2024.108619 |
| 英文摘要 | With the escalating development and utilization of subterranean spaces, the seismic hazards faced by underground structures are progressively increasing. However, owing to the challenges associated with acquiring underground seismic data and historical seismic design norms, pertinent regulations and research in this domain are scarce. This study focused on three crucial intensity measures in the seismic design process of underground structures peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD). The research leverages seismic data obtained from the California Strong Motion Instrumentation Program (CSMIP) to train and evaluate a conditional generative adversarial network (CGAN) model. This model was employed to establish a multivariate joint conditional probability distribution among the intensity measures at varying depths, facilitating the stochastic prediction of shallow intensity measures. In contrast to empirical formulas, the CGAN model eliminates the need for a predefined equation structure and enables the simultaneous prediction of multiple intensity measures. The performance of the model was evaluated by comparing the predictive accuracy of the CGAN model and empirical fitting formulas across diverse site conditions and depth intervals using metrics such as relative error coefficients. It can be concluded that the proposed CGAN model can accurately predict shallow seismic intensity measures, and the predictions conform to a specific conditional distribution while retaining the stochastic nature of seismic motion. Compared with empirical formula models, the CGAN model exhibited an enhanced predictive capability. |
| 资助项目 | Natural Science Foundation of Henan Province[242300421057] ; National Natural Science Foundation of China[52279114] ; National Natural Science Foundation of China[51909241] ; National Natural Science Foundation of China[52308475] ; National Natural Science Foundation of China[52008376] ; Young Elite Scientists Sponsorship Program by CAST[2023QNRC001] ; Henan Province Science and Technology Innovation Talent Program[2023HYTP002] |
| WOS研究方向 | Engineering ; Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001289791800001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42171]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Shen, Jiaxu |
| 作者单位 | 1.Zhengzhou Univ, Sch Civil Engn, Zhengzhou 450001, Henan, Peoples R China 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Beijing Univ Technol, Fac Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China 5.Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing 100124, Peoples R China |
| 推荐引用方式 GB/T 7714 | Duan, Shuqian,Song, Zebin,Shen, Jiaxu,et al. Prediction for underground seismic intensity measures using conditional generative adversarial networks[J]. SOIL DYNAMICS AND EARTHQUAKE ENGINEERING,2024,180:14. |
| APA | Duan, Shuqian,Song, Zebin,Shen, Jiaxu,&Xiong, Jiecheng.(2024).Prediction for underground seismic intensity measures using conditional generative adversarial networks.SOIL DYNAMICS AND EARTHQUAKE ENGINEERING,180,14. |
| MLA | Duan, Shuqian,et al."Prediction for underground seismic intensity measures using conditional generative adversarial networks".SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 180(2024):14. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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