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
Reference10 articles.
1. Development of LSTM Networks for Predicting Viscoplasticity with Effects of Deformation, Strain Rate, and Temperature History;Benabou L.;Journal of Applied Mechanics,2021
2. Learning long-term dependencies with gradient descent is difficult
3. Boulanger, R. W., and Ziotopoulou, K. (2017). PM4Sand (Version 3.1): A Sand Plasticity Model for Earthquake Engineering Applications. Center for Geotechnical Modeling, University of California, Davis, CA.
4. Long Short-Term Memory
5. Pore-Pressure Rise Mechanism and Soil Liquefaction
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