Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach
Abstract
:1. Introduction
2. Earthquake Data
3. Prediction Models
3.1. Physics-Based Model
3.2. One-Dimensional Convolutional Neural Network (CNN)-Based Model
3.2.1. Overview
3.2.2. Model Architecture
3.3. Long Short-Term Memory (LSTM) Networks-Based Model
3.3.1. Overview
3.3.2. Model Architecture
3.4. Transformer-Based Model
3.4.1. Overview
3.4.2. Model Architecture
4. Training
5. Results and Discussion
5.1. Prediction Performance
5.1.1. Response Spectra
5.1.2. Prediction Error
5.2. Computational Performance
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Site ID | Site Name | (m/s) | (s) | NEHRP Site Classification | Description |
---|---|---|---|---|---|
FKSH17 | Kawamata | 544 | 0.22 | C | Dense soil, soft rock |
FKSH18 | Miharu | 307.2 | 0.39 | D | Stiff soil |
FKSH19 | Miyakoji | 338.1 | 0.35 | D | Stiff soil |
IBRH13 | Takahagi | 335.4 | 0.36 | D | Stiff soil |
IWTH02 | Tamayama | 816.3 | 0.15 | B | Rock |
IWTH05 | Fujisawa | 442.1 | 0.27 | C | Dense soil, soft rock |
IWTH12 | Kunohe | 367.9 | 0.33 | C | Dense soil, soft rock |
IWTH14 | Taro | 816.3 | 0.15 | B | Rock |
IWTH21 | Yamada | 521.1 | 0.23 | C | Dense soil, soft rock |
IWTH22 | Towa | 532.1 | 0.23 | C | Dense soil, soft rock |
IWTH27 | Rikuzentakata | 670.3 | 0.18 | C | Dense soil, soft rock |
MYGH04 | Towa | 849.8 | 0.14 | B | Rock |
Site | CNN | LSTM | Transformer | SHAKE |
---|---|---|---|---|
FKSH17 | 0.000129 | 0.000092 | 0.000101 | 0.001697 |
FKSH18 | 0.000391 | 0.000177 | 0.000156 | 0.009639 |
FKSH19 | 0.009381 | 0.012412 | 0.009521 | 0.120766 |
IBRH13 | 0.007119 | 0.009947 | 0.008425 | 0.013708 |
IWTH02 | 0.002300 | 0.000726 | 0.000830 | 0.004351 |
IWTH05 | 0.004891 | 0.003666 | 0.003003 | 0.005632 |
IWTH12 | 0.003959 | 0.002878 | 0.004252 | 0.076996 |
IWTH14 | 0.000458 | 0.000163 | 0.000139 | 0.001461 |
IWTH21 | 0.000830 | 0.000689 | 0.001646 | 0.006386 |
IWTH22 | 0.000529 | 0.000830 | 0.000315 | 0.048460 |
IWTH27 | 0.000824 | 0.000750 | 0.000536 | 0.002810 |
MYGH04 | 0.004510 | 0.002340 | 0.002776 | 0.064900 |
Avg. | 0.002943 | 0.002889 | 0.002642 | 0.029734 |
CNN Model (s) | LSTM Model (s) | Transformer Model (s) |
---|---|---|
0.3830 | 0.4087 | 0.3894 |
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Choi, Y.; Nguyen, H.-T.; Han, T.H.; Choi, Y.; Ahn, J. Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach. Appl. Sci. 2024, 14, 6658. https://doi.org/10.3390/app14156658
Choi Y, Nguyen H-T, Han TH, Choi Y, Ahn J. Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach. Applied Sciences. 2024; 14(15):6658. https://doi.org/10.3390/app14156658
Chicago/Turabian StyleChoi, Yongjin, Huyen-Tram Nguyen, Taek Hee Han, Youngjin Choi, and Jaehun Ahn. 2024. "Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach" Applied Sciences 14, no. 15: 6658. https://doi.org/10.3390/app14156658
APA StyleChoi, Y., Nguyen, H.-T., Han, T. H., Choi, Y., & Ahn, J. (2024). Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach. Applied Sciences, 14(15), 6658. https://doi.org/10.3390/app14156658