Open Access
| Issue |
E3S Web Conf.
Volume 730, 2026
International Conference on Advances and Innovations in Soft Soil Engineering (Soft Soils 2026)
|
|
|---|---|---|
| Article Number | 03006 | |
| Number of page(s) | 6 | |
| Section | Constitutive, Numerical, and Machine Learning Models | |
| DOI | https://doi.org/10.1051/e3sconf/202673003006 | |
| Published online | 03 August 2026 | |
- M. Beck, K. Pöppel, M. Spanring, A. Auer, O. Prudnikova, M. Kopp, G. Klambauer, J. Brandstetter, S. Hochreiter, xLSTM: extended long short-term memory, in Advances in Neural Information Processing Systems 37 (NeurIPS 2024), Vancouver, Canada, December 10– 15 (2024). https://arxiv.org/abs/2405.04517 [Google Scholar]
- K.H. Roscoe, J.B. Burland, On the generalised stress– strain behaviour of ‘wet’ clay, in Engineering Plasticity, ed. by J. Heyman, F.A. Leckie (Cambridge University Press, Cambridge, 1968), pp. 535–609 [Google Scholar]
- M. Taiebat, Y.F. Dafalias, SANISAND: simple anisotropic sand plasticity model. Int. J. Numer. Anal. Methods Geomech. 32, 915–948 (2008). https://doi.org/10.1002/nag.651 [Google Scholar]
- N. Zhang, S.-L. Shen, A. Zhou, Y.-F. Jin, Application of LSTM approach for modelling stress–strain behaviour of soil. Appl. Soft Comput. 100, 106959 (2021). https://doi.org/10.1016/j.asoc.2020.106959 [Google Scholar]
- L. Hadam, M. Amrane, N. Handel, R. Demagh, S. Messast, A. Gheris, Deep sequence learning architectures for predicting cyclic soil stress-strain behavior: comparative evaluation of LSTM, GRU, TCN, and hybrid models. Transp. Geotech., 101822 (2025). https://doi.org/10.1016/j.trgeo.2025.101822 [Google Scholar]
- S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9, 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735 [CrossRef] [Google Scholar]
- F. Masi, I. Stefanou, P. Vannucci, V. Maffi-Berthier, Material modeling via thermodynamics-based artificial neural networks, in Geometric Structures of Statistical Physics, Information Geometry, and Learning (SPIGL’20), Springer Proceedings in Mathematics & Statistics, vol. 361, ed. by F. Barbaresco, F. Nielsen (Springer, Cham, 2021), pp. 308–329. https://doi.org/10.1007/978-3-030-77957-3_16 [Google Scholar]
- M.M. Su, Y. Yu, T.H. Chen, N. Guo, Z.X. Yang, A thermodynamics-informed neural network for elastoplastic constitutive modeling of granular materials. Comput. Methods Appl. Mech. Eng. 430, 117246 (2024). https://doi.org/10.1016/j.cma.2024.117246 [Google Scholar]
- T. Wichtmann, T. Triantafyllidis, An experimental database for the development, calibration and verification of constitutive models for sand with focus to cyclic loading: part I—tests with monotonic loading and stress cycles. Acta Geotech. 11, 739–761 (2016). https://doi.org/10.1007/s11440-015-0402-z [Google Scholar]
- S. Bengio, O. Vinyals, N. Jaitly, N. Shazeer, Scheduled sampling for sequence prediction with recurrent neural networks, in Advances in Neural Information Processing Systems 28 (NIPS 2015), Montréal, Canada, December 7–12 (2015). https://arxiv.org/abs/1506.03099 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.