Multi-fidelity data-driven modelling of rate-dependent behaviour of soft clays

References

• Duchi, J., E. Hazan, and Y. Singer. 2011. “Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.” Journal of Machine Learning Research 12 (7): 2121–2159.
   Google Scholar
• Feng, W. Q., J. H. Yin, X. M. Tao, F. Tong, and W. B. Chen. 2017. “Time and Strain-Rate Effects on Viscous Strain-Strain Behavior of Plasticine Material.” International Journal of Geomechanics 17 (5): 04016115. doi:10.1061/(ASCE)GM.1943-5622.0000806.
   Web of Science ®Google Scholar
• Forrester, A. I. J., N. W. Bressloff, and A. J. Keane. 2006. “Optimization Using Surrogate Models and Partially Converged Computational Fluid Dynamics Simulations.” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 462 (2071): 2177–2204. doi:10.1098/rspa.2006.1679.
   Web of Science ®Google Scholar
• Guo, Z., L. Song, C. Park, J. Li, and R. T. Haftka. 2018. “Analysis of Dataset Selection for Multi-Fidelity Surrogates for a Turbine Problem.” Structural and Multidisciplinary Optimization 57 (6): 2127–2142. doi:10.1007/s00158-018-2001-8.
   Web of Science ®Google Scholar
• Han, J., Z. Y. Yin, C. Dano, and P. Y. Hicher. 2021. “Effect of Strain Rate on the Adhesive Bond Shearing Resistance of Stiff Clay.” Transportation Geotechnics 27: 100479. doi:10.1016/j.trgeo.2020.100479.
   Web of Science ®Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Karstunen, M., M. Rezania, N. Sivasithamparam, and Z. Y. Yin. 2015. “Comparison of Anisotropic Rate-Dependent Models for Modeling Consolidation of Soft Clays.” International Journal of Geomechanics 15 (5): A4014003. doi:10.1061/(ASCE)GM.1943-5622.0000267.
   Web of Science ®Google Scholar
• Kingma, D. P., and J. Ba. 2014. “Adam: A Method for Stochastic Optimization.” arXiv:1412.6980.
   Google Scholar
• Kohavi, R. 1995. “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.” Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, Morgan Kaufmann Publishers Inc 2: 1137–1143.
   Google Scholar
• Liu, Z. Q., D. Guo, S. Lacasse, J. H. Li, B. B. Yang, and J. C. Choi. 2020. “Algorithms for Intelligent Prediction of Landslide Displacements.” Journal of Zhejiang University-SCIENCE A 21 (6): 412–429. doi:10.1631/JZUS.A2000005.
   Web of Science ®Google Scholar
• Liu, X., S. Tian, F. Tao, and W. Yu. 2021. “A Review of Artificial Neural Networks in the Constitutive Modeling of Composite Materials.” Composites Part B: Engineering 224: 109152. doi:10.1016/j.compositesb.2021.109152.
   Web of Science ®Google Scholar
• Meng, X., and G. E. Karniadakis. 2020. “A Composite Neural Network That Learns from Multi-Fidelity Data: Application to Function Approximation and Inverse PDE Problems.” Journal of Computational Physics 401: 109020. doi:10.1016/j.jcp.2019.109020.
   Web of Science ®Google Scholar
• Moler, C. B. 2004. Numerical Computing with MATLAB. Philadelphia: SIAM.
   Google Scholar
• Najjar, Y. M., and C. Huang. 2007. “Simulating the Stress-Strain Behavior of Georgia Kaolin Via Recurrent Neuronet Approach.” Computers and Geotechnics 34 (5): 346–361. doi:10.1016/j.compgeo.2007.06.006.
   Web of Science ®Google Scholar
• Qian, P. Z. G., and C. F. J. Wu. 2008. “Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments.” Technometrics 50 (2): 192–204. doi:10.1198/004017008000000082.
   Web of Science ®Google Scholar
• Ruder, S. 2016. “An Overview of Gradient Descent Optimization Algorithms.” arXiv:1609.04747.
   Google Scholar
• Sachan, A., and D. Penumadu. 2007. “Effect of Microfabric on Shear Behavior of Kaolin Clay.” Journal of Geotechnical and Geoenvironmental Engineering 133 (3): 306–318. doi:10.1061/(ASCE)1090-0241(2007)133:3(306).
   Web of Science ®Google Scholar
• Shen, S. L., N. Zhang, A. Zhou, and Z. Y. Yin. 2022. “Enhancement of Neural Networks with an Alternative Activation Function TanhLU.” Expert Systems with Applications 199: 117181. doi:10.1016/j.eswa.2022.117181.
   Web of Science ®Google Scholar
• Xu, D., S. Zhang, H. Zhang, and D. P. Mandic. 2021. “Convergence of the RMSProp Deep Learning Method with Penalty for Nonconvex Optimization.” Neural Networks 139: 17–23. doi:10.1016/j.neunet.2021.02.011.
   PubMed Web of Science ®Google Scholar
• Yin, Z. Y., J. Li, Y. F. Jin, and F. Y. Liu. 2019. “Estimation of Robustness of Time Integration Algorithms for Elasto-Viscoplastic Modeling of Soils.” International Journal of Geomechanics 19 (2): 04018197. doi:10.1061/(ASCE)GM.1943-5622.0001351.
   Web of Science ®Google Scholar
• Yin, J. H., J. G. Zhu, and J. Graham. 2002. “A New Elastic Viscoplastic Model for Time-Dependent Behaviour of Normally and Overconsolidated Clays: Theory and Verification.” Canadian Geotechnical Journal 39 (1): 157–173. doi:10.1139/t01-074.
   Web of Science ®Google Scholar
• Zhang, Y., J. F. Schutte, W. P. Seneviratne, N. H. Kim, and R. T. Haftka. 2017. “Sampling by Exploration and Replication for Estimating Experimental Strength of Composite Structures.” AIAA Journal 55 (10): 3594–3602. doi:10.2514/1.J055862.
   Web of Science ®Google Scholar
• Zhang, N., S. L. Shen, A. Zhou, and Y. F. Jin. 2021a. “Application of LSTM Approach for Modelling Stress-Strain Behaviour of Soil.” Applied Soft Computing 100: 106959. doi:10.1016/j.asoc.2020.106959.
   Web of Science ®Google Scholar
• Zhang, P., Z. Y. Yin, and Y. F. Jin. 2021. “State-of-the-Art Review of Machine Learning Applications in Constitutive Modeling of Soils.” Archives of Computational Methods in Engineering 28 (5): 3661–3686. doi:10.1007/s11831-020-09524-z.
   Web of Science ®Google Scholar
• Zhang, P., Z. Y. Yin, and Y. F. Jin. 2022c. “Bayesian Neural Network-Based Uncertainty Modelling: Application to Soil Compressibility and Undrained Shear Strength Prediction.” Canadian Geotechnical Journal 59 (4): 546–557. doi:10.1139/cgj-2020-0751.
   Web of Science ®Google Scholar
• Zhang, P., Z. Y. Yin, Y. F. Jin, and B. Sheil. 2022b. “Physics-Constrained Hierarchical Data-Driven Modelling Framework for Complex Path-Dependent Behaviour of Soils.” International Journal for Numerical and Analytical Methods in Geomechanics 46 (10): 1831–1850. doi:10.1002/nag.3370.
   Web of Science ®Google Scholar
• Zhang, P., Z. Y. Yin, Y. F. Jin, J. Yang, and B. Sheil. 2022a. “Physics-Informed Multifidelity Residual Neural Networks for Hydromechanical Modeling of Granular Soils and Foundation Considering Internal Erosion.” Journal of Engineering Mechanics 148 (4): 04022015. doi:10.1061/(ASCE)EM.1943-7889.0002094.
   Web of Science ®Google Scholar
• Zhang, P., Z. Y. Yin, Y. F. Jin, and G. L. Ye. 2020. “An AI-Based Model for Describing Cyclic Characteristics of Granular Materials.” International Journal for Numerical and Analytical Methods in Geomechanics 44 (9): 1315–1335. doi:10.1002/nag.3063.
   Web of Science ®Google Scholar
• Zhang, N., N. Zhang, Q. Zheng, and Y. S. Xu. 2021b. “Real-Time Prediction of Shield Moving Trajectory During Tunnelling Using GRU Deep Neural Network.” Acta Geotechnica 17 (4): 1167–1182. doi:10.1007/s11440-021-01319-1.
   Web of Science ®Google Scholar
• Zhu, J. G., and J. H. Yin. 2000. “Strain-Rate-Dependent Stress-Strain Behavior of Overconsolidated Hong Kong Marine Clay.” Canadian Geotechnical Journal 37 (6): 1272-1282. doi:10.1139/t00-054.
   Web of Science ®Google Scholar