A Machine Learning Approach to Predicting Pore Pressure Response in Liquefiable Sands under Cyclic Loading
Author:
Affiliation:
1. Dept. of Civil, Architectural, and Environmental Engineering, Univ. of Texas at Austin, TX.
Publisher
American Society of Civil Engineers
Link
https://ascelibrary.org/doi/pdf/10.1061/9780784484692.021
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4. Long Short-Term Memory
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1. Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach;Applied Sciences;2024-07-30
2. Prediction of shear strain and excess pore water pressure response in liquefiable sands under cyclic loading using deep learning model;Japanese Geotechnical Society Special Publication;2024
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