EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 730 (2026) E3S Web Conf., 730 (2026) 03003 References
Open Access
Issue
E3S Web Conf.
Volume 730, 2026
International Conference on Advances and Innovations in Soft Soil Engineering (Soft Soils 2026)
Article Number 03003
Number of page(s) 8
Section Constitutive, Numerical, and Machine Learning Models
DOI https://doi.org/10.1051/e3sconf/202673003003
Published online 03 August 2026
  1. M. Carter, S.P. Bentley. Soil properties and their correlations. John Wiley & Sons, Ltd, 2nd Edition (2016) [Google Scholar]
  2. A. Casagrande. The liquid limit of soils. ASTM Proceedings 48, 1033–1049 (1948) [Google Scholar]
  3. R.D. Holtz, W.D. Kovacs. An introduction to geotechnical engineering. Prentice Hall, New Jersey (1981) [Google Scholar]
  4. K. Terzaghi, R.B. Peck. Soil mechanics in engineering practice. John Wiley, London (1967) [Google Scholar]
  5. A.W. Skempton. The pore pressure coefficients A and B. Geotechnique 4(4), 143–147 (1954) [CrossRef] [Google Scholar]
  6. R. Singh, D.J. Henkel, D.A. Sangay. Shear and K0 swelling of overconsolidated clay. Proc. 8th ICSMFE, vol. 1, 367–376 (1973) [Google Scholar]
  7. A.W. Skempton, R.D. Northey. The sensitivity of clays. Géotechnique 3, 305–353 (1952) [Google Scholar]
  8. L. Bjerrum, N.E. Simons. Comparison of shear strength characteristics of normally consolidated clays. Proc. ASCE Research Conf. on Shear Strength of Cohesive Soils, Boulder, 711-726 (1960) [Google Scholar]
  9. British Standards Institution. Methods of test for soils for civil engineering purposes. BS 1377 (1990) [Google Scholar]
  10. ASTM International. Standard test methods for liquid limit, plastic limit, and plasticity index of soils. ASTM D4318 [Google Scholar]
  11. S.F.F. Mojtahedi, I. Ebtehaj, M. Hasanipanah, H. Bonakdari, H.B. Amnieh. Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Eng. Comput. 35, 47–56 (2019) [Google Scholar]
  12. N. Yousefpour, F.F. Mojtahedi. Early detection of internal erosion in earth dams: combining seismic monitoring and convolutional AutoEncoders. Georisk (2023): 1–21 [Google Scholar]
  13. W. Chen, P. Xie, J. Gu, H. Wang. A comparative study of ensemble learning methods for predicting the uniaxial tensile strength of expansive soil. Eng. Comput. 39, 2163–2178 (2022) [Google Scholar]
  14. W. Chen, J. Zhang, H. Wang. Prediction of the shear strength of fly ash-amended soils with lignin reinforcement using ensemble learning algorithms. Constr. Build. Mater. 369, 130583 (2023) [Google Scholar]
  15. M. Mahmoudi, G. Spagnoli, S. Shimobe. Integrating Bayesian and Particle Swarm Optimization in Ensemble Machine Learning for Enhanced Prediction of Soil Liquidity Index and Shear Strength. Int. J. Geosynth. Ground Eng. 11, 50 (2025). https://doi.org/10.1007/s40891-025-00656-5 [Google Scholar]
  16. G.E.A.P.A. Batista, M.C. Monard. An analysis of four missing data treatment methods for supervised learning. Appl. Artif. Intell. 17, 519–533 (2003) [Google Scholar]
  17. L. Beretta, A. Santaniello. Nearest neighbor imputation algorithms: a critical evaluation. BMC Med. Inform. Decis. Mak. 16, 74 (2016) [Google Scholar]
  18. F. Farooq, A. Czarnecki, M.U. Akbar Nizami, K. Javed, et al. A comparative study for the prediction of the compressive strength of self-compacting concrete modified with fly ash. Materials 14, 4934 (2021) [CrossRef] [PubMed] [Google Scholar]
  19. V. Nourani, G. Elkiran, S.I. Abba. Wastewater treatment plant performance analysis using artificial intelligence-- an ensemble approach. Water Sci. Technol. 78, 2064–2076 (2018) [Google Scholar]
  20. M.C. Popescu, V.E. Balas, L. Perescu-Popescu, N. Mastorakis. Multilayer perceptron and neural networks. WSEAS Trans. Circuits Syst. 8, 579–588 (2009) [Google Scholar]
  21. L. Breiman. Random forests. Mach. Learn. 45, 5–32 (2001) [Google Scholar]
  22. R. Genuer, J.M. Poggi, C. Tuleau-Malot, N. Villa-Vialaneix. Random forests for big data. Big Data Res. 9, 28–46 (2020) [Google Scholar]
  23. D.P. Solomatine, D.L. Shrestha. AdaBoost.RT: a boosting algorithm for regression problems. 2004 IEEE Int. Joint Conf. Neural Netw., vol. 2, 1163–1168 (2004) [Google Scholar]
  24. D.L. Shrestha, D.P. Solomatine. Experiments with AdaBoost.RT, an improved boosting scheme for regression. Neural Comput. 18, 1678–1710 (2006) [Google Scholar]
  25. M. Awad, R. Khanna, M. Awad, R. Khanna. Support vector regression. In: Efficient learning machines (Springer, Berkeley, 2015), pp. 67–80 [Google Scholar]
  26. P.R. Lorenzo, J. Nalepa, L.S. Ramos, J.R. Pastor. Hyper-parameter selection in deep neural networks using parallel particle swarm optimization. Proc. Genetic Evol. Comput. Conf. Companion, 1864-1871 (2017) [Google Scholar]
  27. R. Eberhart, J. Kennedy. A new optimizer using particle swarm theory. MHS’95 Proc. Sixth Int. Symp. Micro Mach. Hum. Sci., 39-43 (1995) [Google Scholar]
  28. L. Tani, C. Veelken. Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics. Comput. Phys. Commun. 294, 108955 (2024). https://doi.org/10.1016/j.cpc.2023.108955 [Google Scholar]
  29. M. Fang, C. Pan, X. Yu, W. Li, B. Wang, H. Zhou, Z. Xu, G. Yang. Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests. BMC Med. Inform. Decis. Mak. 25, 131 (2025). https://doi.org/10.1186/s12911-025-02956-2 [Google Scholar]
  30. A. Chauhan, Shivaprakash S.J., Sabireen H., Abdul Quadir Md., Neelanarayanan Venkataraman. Stock price forecasting using PSO hypertuned neural nets and ensembling. Appl. Soft Comput. 147, 110835 (2023). https://doi.org/10.1016/j.asoc.2023.110835 [Google Scholar]
  31. S. Hansbo. A new approach to the determination of the shear strength of clay by the fall-cone test. Proc. Royal Swedish Geotech. Inst. 14, 1–47 (1957) [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.