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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.20186 |
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| _version_ | 1866912087989551104 |
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| author | Meng, Shiqiao Zhou, Ying Zheng, Qinghua Liao, Bingxu Chang, Mushi Zhang, Tianshu Djerrad, Abderrahim |
| author_facet | Meng, Shiqiao Zhou, Ying Zheng, Qinghua Liao, Bingxu Chang, Mushi Zhang, Tianshu Djerrad, Abderrahim |
| contents | Accurately predicting the dynamic responses of building structures under seismic loads is essential for ensuring structural safety and minimizing potential damage. This critical aspect of structural analysis allows engineers to evaluate how structures perform under various loading conditions, facilitating informed design and safety decisions. Traditional methods, which rely on complex finite element models often struggle with balancing computational efficiency and accuracy. To address this challenge, we introduce SeisGPT, a data-driven, large physics-informed model that leverages deep neural networks based on the Generative Pre-trained Transformer (GPT) architecture. SeisGPT is designed to predict, in real-time the dynamic behavior of building structures under seismic forces. Trained on a diverse corpus of seismic data and structural engineering principles, it instantly generates predictive responses, including displacement, acceleration, and inter-story drift, with high accuracy and computational efficiency. Its adaptability across various building typologies and seismic intensities makes this framework a valuable tool for designing robust structures and assessing seismic risk. Through comprehensive validation, this approach exhibits superior performance, offering engineers and researchers a powerful tool for assessing seismic response and informing resilient design strategies. This innovative framework represents a significant advancement in seismic engineering practice, with potential applications in mitigating seismic hazards and enhancing structural resilience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20186 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SeisGPT: A Physics-Informed Data-Driven Large Model for Real-Time Seismic Response Prediction Meng, Shiqiao Zhou, Ying Zheng, Qinghua Liao, Bingxu Chang, Mushi Zhang, Tianshu Djerrad, Abderrahim Computational Engineering, Finance, and Science Accurately predicting the dynamic responses of building structures under seismic loads is essential for ensuring structural safety and minimizing potential damage. This critical aspect of structural analysis allows engineers to evaluate how structures perform under various loading conditions, facilitating informed design and safety decisions. Traditional methods, which rely on complex finite element models often struggle with balancing computational efficiency and accuracy. To address this challenge, we introduce SeisGPT, a data-driven, large physics-informed model that leverages deep neural networks based on the Generative Pre-trained Transformer (GPT) architecture. SeisGPT is designed to predict, in real-time the dynamic behavior of building structures under seismic forces. Trained on a diverse corpus of seismic data and structural engineering principles, it instantly generates predictive responses, including displacement, acceleration, and inter-story drift, with high accuracy and computational efficiency. Its adaptability across various building typologies and seismic intensities makes this framework a valuable tool for designing robust structures and assessing seismic risk. Through comprehensive validation, this approach exhibits superior performance, offering engineers and researchers a powerful tool for assessing seismic response and informing resilient design strategies. This innovative framework represents a significant advancement in seismic engineering practice, with potential applications in mitigating seismic hazards and enhancing structural resilience. |
| title | SeisGPT: A Physics-Informed Data-Driven Large Model for Real-Time Seismic Response Prediction |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2410.20186 |