Abstract
The artificial intelligence is a revolutionary technology applied in many fields, and these fields are medical science, space science, and engineering. Artificial intelligence has two subsets; one is machine learning, and the second is deep learning. In this technical article, the regression analysis, multilayer perceptron class-based neural network models are employed to predict the compactive parameters (optimum moisture content and maximum dry density) of soil in MATLAB R2020a. Various neural network models are developed using different hyperparameters, i.e. hidden layers (one to five), neurons (two to eleven). The performance of the proposed ANN models is compared to determine the best architectural ANN models. The ANN_OMC_3H8 and ANN_MDD_5H1 NN models are determined as the best architectural ANN models and compared to regression model to obtain the optimum performance model. From the comparison, it is observed that the artificial neural network outperformed the regression model. Hence, the proposed artificial neural network models, namely ANN_OMC_3H8 and ANN_MDD_5H1, have the potential to predict the OMC and MDD of soil, respectively.
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References
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
Shrivastava AK, Jain PK (2016) Prediction of compaction parameters using regression and ANN tools. Int J Sci Res Dev 3(11):697–702
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
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
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
Hasnat A, Hasan MdM, Islam MdR, Alim MdA (2019) Prediction of compaction parameters of soil using support vector machine. Curr Trends Civil Struct Eng 4(1):1–7
Patra CR, Sivakugan N, Das BM, Rout SK (2013) Correlations for relative density of clean sand with median grain size and compaction energy. Int J Geotech Eng 4(2):195–203
Arora KR (2004) Soil mechanics and foundation engineering. Standard Publisher Distributors, New Delhi, India
Smith GN (1986) Probability and statistics in civil engineering: an introduction. Collins, London
Wang H-L, Yin Z-Y (2020) High performance prediction of soil compaction parameters using multi expression programming. Eng Geol 279:105758
Mujtaba H, Farooq K, Sivakugan N, Das BM (2013) Correlation between gradational parameters and compaction characteristics of sandy soils. Int J Geotech Eng 7(4):395–401
Jayan J, Sankar N (2015) Prediction of compaction parameters of soils using artificial neural network. Asian J Eng Technol 3(4):368–375, special issue for ICETTAS’15
Farooq K, Khalid U, Mujtaba H (2016) Prediction of compaction characteristics of fine-grained soil using consistency limits. Arab J Sci Eng 41:1319–1928
Ng KS, Chew YM, Osman MH, Mohamad Ghazali SK (2015) Estimating maximum dry density and optimum moisture content of compacted soils. In: International conference on advances in civil and environmental engineering, Faculty of Civil Engineering, Universiti Teknologi MARA Pulau Pinang, July 2015
Sanuade OA, Adesina RB, Amosun JO, Fajana AO, Olaseeni OG (2017) Using artificial neural network to predict dry density of soil from thermal conductivity. Mater Geoenviron 64(3):169–180
Sivrikaya O, Kayadelen C, Cecen E (2013) Prediction of the compaction parameters for coarse-grained soil with fines content by MLR and GEP. Acta Geotechnica Slovenica 10(2):29–41
Al-saffar RZ, Khattab S, Yusif ST (2013) Prediction of soil’s compaction parameters using artificial neural network. Al-Rafidain Eng 21(3):15–27
Suman S, Mahamaya M, Das SK (2016) Prediction of maximum dry density and unconfined compressive strength of cemented stabilized soil using artificial intelligence techniques. Int J Geosynth Ground Eng 2(11):1–11
Khuntia S, Mujtaba H, Patra C, Farooq K, Sivakugan N, Das BM (2015) Prediction of compaction parameters of coarse-grained soil using multivariate adaptive regression splines (MARS). Int J Geotech Eng 9(1):79–88
Khalid U, Ur Rehman Z (2018) Evaluation of compaction parameters of fine-grained soil using standard and modified efforts. Int J Geo-Eng 15(9):1–17
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Khatti, J., Grover, K.S. (2023). Evaluation of Compactive Parameters of Soil Using Machine Learning. In: Muthukkumaran, K., Ayothiraman, R., Kolathayar, S. (eds) Soil Dynamics, Earthquake and Computational Geotechnical Engineering. IGC 2021. Lecture Notes in Civil Engineering, vol 300. Springer, Singapore. https://doi.org/10.1007/978-981-19-6998-0_1
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DOI: https://doi.org/10.1007/978-981-19-6998-0_1
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