Evaluation of Compactive Parameters of Soil Using Machine Learning

Author:

Khatti JitendraORCID,Grover Kamaldeep SinghORCID

Publisher

Springer Nature Singapore

Reference20 articles.

1. Salahudeen AB, Ijimdiya TS, Eberemu AO, Osinubi KJ (2018) Artificial neural networks prediction of compaction characteristics of black cotton soil stabilized with cement kiln dust. J Soft Comput Civil Eng 2(3):50–71

2. Shrivastava AK, Jain PK (2016) Prediction of compaction parameters using regression and ANN tools. Int J Sci Res Dev 3(11):697–702

3. Majidi A, Lashgaripour G, Shoaie Z, Noruzi Nashlaji M, Firouzei Y (2014) Estimating compaction parameters of marl soils using multi-layer perceptron neural networks. J Balkan Tribological Assoc 20(2):170–198

4. Alavi AH, Gandomi AH, Mollahassani A, Heshmati AA, Rashed A (2010) Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks. J Plant Nutr Soil Sci 173(3):368–379

5. Anjita NA, George CA, Krishnankutty SV (2017) Prediction of maximum dry density of soil using Genetic algorithm. Int J Eng Res Technol 6(3):550–552

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Hybrid neuro-fuzzy models for assessing the optimum moisture content of lime cement-treated soil;Multiscale and Multidisciplinary Modeling, Experiments and Design;2024-06-28

2. Hybrid and individual least square support vector regression methods for estimating the optimal moisture content of stabilized soil;Multiscale and Multidisciplinary Modeling, Experiments and Design;2024-02-14

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