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Ground motion model for acceleration response spectra using conditional-generative adversarial network
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DOI：10.1007/s11069-024-06988-1.png)
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摘要
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The present study focuses on developing a ground motion model (GMM) for 5%-damped spectral acceleration (Sa) using a Conditional Generative Adversarial Network (C-GAN). Unlike traditional methods, the model incorporates the physics of source, path, and site characteristics into the adversarial training process between the generator and discriminator. The model is trained on a comprehensive dataset comprising 23,929 ground motion records from both horizontal and vertical directions, sourced from 325 shallow crustal events in the updated NGA-West2 database. The input parameters include the moment magnitude (Mw), Joyner-Boore distance (RJB), the focal mechanism (F), hypocentral depth (Hd), average shear-wave velocity up to 30 m depth (Vs30), and the direction of Sa (dir). To ensure the model's integrity, an inter-event and intra-event residual analysis is conducted, validating its robustness and unbiasedness. Additionally, the model's performance is evaluated against established GMMs relevant to similar seismo-tectonic backgrounds. Moreover, the applicability of the developed model is demonstrated through the estimation of site-specific response spectra for Chi-Chi, Taiwan and Loma Prieta. Thus, the study contributes to advancing ground motion modelling techniques applicable in seismic hazard assessment and structural engineering practices.
Keyword:
C-GAN
Ground motion prediction
NGA-West2
GMM
Response spectra
期刊
IF:
3.7
论文数:
9.7K
被引数:
2.8W
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引用论文
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