ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access

Determination of the Optimum Performance AI Model and Methodology to Predict the Compaction Parameters of Soils


Affiliations
1 Department of Department of Civil Engineering, Rajasthan Technical University, India
 

   Subscribe/Renew Journal


This technical article helps identify the optimum performance AI model for predicting compaction parameters of soil. A comparative study is mapped between regression analysis (RA), Gaussian process regression (GPR), decision tree (DT), support vector machine (SVM), and artificial neural networks (ANNs) approaches using 59 soil datasets. The soil dataset consists of soil properties such as gravel content, silt content, sand content, specific gravity, clay content, plasticity index, and liquid limit. The soil properties are used as input parameters to develop the AI model to predict soil optimum moisture content and maximum dry density. The RA, GPR, SVM, DT, and ANN models are designated as MLR_X, GPR_X, SVM_X, DT_X, ANN_X, where the X is OMC and MDD. The performance of MLR_OMC, GPR_OMC, SVM_OMC, DT_OMC, LMNN_OMC, and GDANN_OMC is 0.9714, 0.9867, 0.9689, 0.9832, 0.9435, and 0.9520, respectively. Similarly, the performance of MLR_MDD, GPR_MDD, SVM_MDD, DT_MDD, LMNN_MDD, and GDANN_MDD is 0.9512, 0.9854, 0.9482, 0.9199, 0.8679, and 0.9395, respectively. Based on the performance of AI models, the GPR_OMC and GPR_MDD models are identified as the optimum performance model to predict the soil maximum dry density (MDD) and optimum moisture content (OMC). The predicted OMC and MDD are compared with laboratory OMC and MDD, and it is found that the GPR_OMC and GPR_MDD model has the potential to predict soil compaction parameters.

Keywords

Regression Analysis, Gaussian Process Regression, Support Vector Machine, Artificial Neural Network, Compaction Parameters of Soil
Subscription Login to verify subscription
User
Notifications
Font Size

  • A.B. Salahudeen, T.S. Ijimdiya, A.O. Eberemu and K.J. Osinubi, “Artificial Neural Networks Prediction of Compaction Characteristics of Black Cotton Soil Stabilised with Cement Kiln Dust”, Journal of Soft Computing in Civil Engineering, Vol. 2, No. 3, pp. 50-71, 2018.

  • A.K. Shrivastava and P.K. Jain, “Prediction of Compaction Parameters using Regression and ANN Tools”, International Journal for Scientific Research and Development, Vol. 3, No. 11, pp. 697-702, 2016.

  • G. Majidi, M. Noruzi Nashlaji andd Y. Firouzei, “Estimating Compaction Parameters of Marl Soils using Mult-layer Perceptron Neural Network”, Vol. 20, No. 2, pp. 170-198, 2014.

  • Alireza Ardakani and Afshin Kordnaeij, “Soil Compaction Parameters Prediction using GMDH-type Neural Network and Genetic Algorithm”, European Journal of Environmental and Civil Engineering, Vol. 23, No. 4, pp. 449-462, 2017.

  • Amir Hossein Alavi, Amir Hossein Gandomi, Ali Mollahassani, Ali Akbar Heshmati, Azadeh Rashed, “Modelling of Maximum Dry Density and Optimum Moisture Content of Stabilized Soil using Artificial Neural Networks”, Journal of Plant Nutrition and Soil Science, 173, pp. 368-379, 2010.

  • N.A. Christy Antony George and Soumya V. Krishnankutty, “Prediction of Maximum Dry Density of Soil using Genetic Algorithm”, International Journal of Engineering Research and Technology, Vol. 6, No. 3, pp. 550-552, 2017.

  • Arif Hasnat, Md Mehedi Hasan, Md Rabiul Islam and Md Abdul Alim, “Prediction of Compaction Parameters of Soil using Support Vector Regression”, Current Trend in Civil and Structural Engineering, Vol. 4, No. 1, pp. 1-7, 2019.

  • K.R. Arora, “Soil Mechanics, and Foundation Engineering”, Standard Publisher Distributors, 2004.

  • Suhail I. Khattab and Salim T. Yousif, “Prediction of Soil’s Compaction Parameter using Artificial Neural Network”, Al-Rafidain Engineering, Vol. 21, No. 3, pp. 15-27, 2012.

  • B.G. Farley and W.A. Clark, “Simulation of Self-Organizing Systems by Digital Computer”, IRE Transactions on Information Theory, Vol. 4, No. 4, pp. 76-84, 1954.

  • Gaurav Verma and Brind Kumar, “Prediction of Compaction Parameters for Fine-Grained and CoarseGrained Soils: A Review”, International Journal of Geotechnical Engineering, Vol. 14, No. 8, pp. 970-977, 2019.

  • Han-Lin Wang and Zhen-Yu Yin, “High-Performance Prediction of Soil Compaction Parameters using Multi-expression Programming”, Engineering Geology, Vol. 276, pp. 1-14, 2020.

  • D. Hebb, “The Organization of Behavior”, Wiley, 1949.

  • Jeeja Jayan and N. Sankar, “Prediction of Compaction Parameters of Soils using Artificial Neural Network”, Asian Journal of Engineering and Technology, Vol. 3, No. 4, pp. 368-375, 2015

  • .

  • B. Kaminski and P. Szufel, “A Framework for Sensitivity Analysis of Decision Trees”, Central European Journal of Operations Research, Vol. 26, No. 1, pp. 135-159, 2017.

  • S.C. Kleene, “Representation of Events in Nerve Nets and Finite Automata”, Annals of Mathematics Studies, Vol. 34, pp. 3-41, 1956.

  • Warren McCulloch and Walter Pitts, “A Logical Calculus of Ideas Immanent in Nervous Activity”, Bulletin of Mathematical Biophysics, Vol. 5, No. 4, pp. 115-133, 1943.

  • Osman Gunaydin, Abdurrahman Ozbeyaz and Mehmet Soylemez, “Regression Analysis of Soil Compaction Parameters using Support Vector Method”, Celal Bayar University Journal of Science, Vol. 14, No. 4, pp. 443-447, 2018.

  • Osman Sivrikaya, Cafer Kayadelen and Emre Cecen, “Prediction of the Compaction Parameters for Coarse-Grained Soils with Fine Content by MLR and GEP”, Acta Geotechnica Slovenica, Vol. 2, pp. 29-41, 2013.

  • Shakti Suman, Mahasakti Mahamaya and Sarat Kumar Das, “Prediction of Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilised Soil using Artificial Intelligence Techniques”, International Journal of Geosynthetics and Ground Engineering, Vol. 2, No. 11, pp. 1-11, 2016.

  • G.N. Smith, “Probability and Statistics in Civil Engineering: An Introduction”, Collins, 1986.

  • Sunil Khuntia, Hassan Mujtaba, Chittaranjan Patra, Khalid Farooq, Nagaratnam Sivakugan and Braja M. Das, “Prediction of Compaction Parameters of Coarse-Grained Soil using Multivariate Adaptive Regression Splines (MARS)”, International Journal of Geotechnical Engineering, Vol. 9, No. 1, pp. 79-88, 2015.


Abstract Views: 196

PDF Views: 87




  • Determination of the Optimum Performance AI Model and Methodology to Predict the Compaction Parameters of Soils

Abstract Views: 196  |  PDF Views: 87

Authors

Jitendra Khatti
Department of Department of Civil Engineering, Rajasthan Technical University, India
Kamaldeep Singh Grover
Department of Department of Civil Engineering, Rajasthan Technical University, India

Abstract


This technical article helps identify the optimum performance AI model for predicting compaction parameters of soil. A comparative study is mapped between regression analysis (RA), Gaussian process regression (GPR), decision tree (DT), support vector machine (SVM), and artificial neural networks (ANNs) approaches using 59 soil datasets. The soil dataset consists of soil properties such as gravel content, silt content, sand content, specific gravity, clay content, plasticity index, and liquid limit. The soil properties are used as input parameters to develop the AI model to predict soil optimum moisture content and maximum dry density. The RA, GPR, SVM, DT, and ANN models are designated as MLR_X, GPR_X, SVM_X, DT_X, ANN_X, where the X is OMC and MDD. The performance of MLR_OMC, GPR_OMC, SVM_OMC, DT_OMC, LMNN_OMC, and GDANN_OMC is 0.9714, 0.9867, 0.9689, 0.9832, 0.9435, and 0.9520, respectively. Similarly, the performance of MLR_MDD, GPR_MDD, SVM_MDD, DT_MDD, LMNN_MDD, and GDANN_MDD is 0.9512, 0.9854, 0.9482, 0.9199, 0.8679, and 0.9395, respectively. Based on the performance of AI models, the GPR_OMC and GPR_MDD models are identified as the optimum performance model to predict the soil maximum dry density (MDD) and optimum moisture content (OMC). The predicted OMC and MDD are compared with laboratory OMC and MDD, and it is found that the GPR_OMC and GPR_MDD model has the potential to predict soil compaction parameters.

Keywords


Regression Analysis, Gaussian Process Regression, Support Vector Machine, Artificial Neural Network, Compaction Parameters of Soil

References