Prediction of soil unconfined compressive strength using Artificial Neural Network Model

Hoang-Anh Le, Thuy-Anh Nguyen, Duc-Dam Nguyen, Indra Prakash
Author affiliations


  • Hoang-Anh Le University of Transport Technology, Hanoi 100000, Vietnam
  • Thuy-Anh Nguyen University of Transport Technology, Hanoi 100000, Vietnam
  • Duc-Dam Nguyen University of Transport Technology, Hanoi 100000, Vietnam
  • Indra Prakash Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Gandhinagar 382002, India



soil unconfined compressive strength, Artificial Neural Network, machine learning


The main objective of the present study is to apply Artificial Neural Network (ANN), which is one of the most popular machine learning models, to accurately predict the soil unconfined compressive strength (qu) for the use in designing of foundations of civil engineering structures. For the development of model, data of 118 soil samples were collected from Long Phu 1 power plant project, Soc Trang Province, Vietnam. The database of physicomechanical properties of soils was prepared for the model study, where 70% data was used for the training and 30% for the testing of the model. Standard statistical indices, namely Root Mean Squared Error (RMSE) and Pearson Correlation Coefficient (R) were used in the validation of the model’s performance. In addition, Partial Dependence Plots (PDP) was used to evaluate the importance of the input variables used for modeling. Results showed that the ANN model performed well for the prediction of the qu (RMSE = 0.442 and R = 0.861). The PDP analysis showed that the liquid limit is the most important input factor for modeling of the qu. The present study demonstrated that the ANN is a promising tool that can be used for quick and accurate prediction of the qu, which can be used in designing the civil engineering structures like bridges, buildings, and powerhouses.


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Abad S.V.A.N.K., Yilmaz M., Armaghani D.J., Tugrul A., 2018. Prediction of the durability of limestone aggregates using computational techniques. Neural Computing and Applications, 29, 423–433.

Ahmadlou M., Karimi M., Alizadeh S., Shirzadi A., Parvinnejhad D., Shahabi H., Panahi M., 2019. Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto International, 34, 1252–1272.

Armaghani D.J., Koopialipoor M., Marto A., Yagiz S., 2019. Application of several optimization techniques for estimating TBM advance rate in granitic rocks. Journal of Rock Mechanics and Geotechnical Engineering, 11, 779–789.

Bejarbaneh B.Y., Bejarbaneh E.Y., Fahimifar A., Armaghani D.J., Abd Majid M.Z., 2018. Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bulletin of Engineering Geology and the Environment, 77, 345–361.

Bondi G., Creamer R., Ferrari A., Fenton O., Wall D., 2018. Using machine learning to predict soil bulk density on the basis of visual parameters: Tools for in-field and post-field evaluation. Geoderma, 318, 137–147.

Busscher W.J., Spivey L.D., Campbell R.B., 1987. Estimation of soil strength properties for critical rooting conditions. Soil and Tillage Research, 9, 377–386.

Cokca E., Erol O., Armangil F., 2004. Effects of compaction moisture content on the shear strength of an unsaturated clay. Geotechnical and Geological Engineering, 22, 285.

Dao D.V., Adeli H., Ly H.-B., Le L.M., Le V.M., Le T.-T., Pham B.T., 2020a. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability, 12, 830.

Dao D.V., Ly H.-B., Vu H.-L.T., Le T.-T., Pham B.T., 2020b. Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete. Materials, 13, 1072.

Das B.M., Sobhan K., 2013. Principles of geotechnical engineering. Cengage learning.

Das S.K., Samui P., Sabat A.K., 2011. Application of artificial intelligence to maximum dry density and unconfined compressive strength of cement stabilized soil. Geotechnical and Geological Engineering, 29, 329–342.

Dou J., Yunus A.P., Merghadi A., Shirzadi A., Nguyen H., Hussain Y., Avtar R., Chen Y., Pham B.T., Yamagishi H., 2020. Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Science of the total environment, 720, 137320.

Du Y., Chen Z., Zhang C., Cao X., 2017. Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks. Frontiers of Computer Science, 11, 863–873.

Friedman J., Hastie T., Tibshirani R., 2001. The elements of statistical learning. Springer series in statistics New York.

Kalkan E., Akbulut S., Tortum A., Celik S., 2009. Prediction of the unconfined compressive strength of compacted granular soils by using inference systems. Environmental Geology, 58, 1429–1440.

Khandelwal M., Marto A., Fatemi S.A., Ghoroqi M., Armaghani D.J., Singh T., Tabrizi O., 2018. Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples. Engineering with Computers, 34, 307–317.

Kirts S., Panagopoulos O.P., Xanthopoulos P., Nam B.H., 2018. Soil-compressibility prediction models using machine learning. Journal of Computing in Civil Engineering, 32, 04017067.

Le T.-T., Pham B.T., Ly H.-B., Shirzadi A., Le L.M., 2020. Development of 48-hour Precipitation Forecasting Model using Nonlinear Autoregressive Neural Network, in: Ha-Minh C., Dao D.V., Benboudjema F., Derrible S., Huynh D.V.K., Tang A.M. (Eds.), CIGOS 2019. Innovation for Sustainable Infrastructure, Lecture Notes in Civil Engineering. Springer Singapore, 1191–1196.

Ly H.-B., Le L.M., Phi L.V., Phan V.-H., Tran V.Q., Pham B.T., Le T.-T., Derrible S., 2019a. Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data. Sensors, 19, 4941.

Ly H.-B., Le T.-T., Le L.M., Tran V.Q., Le V.M., Vu H.-L.T., Nguyen Q.H., Pham B.T., 2019b. Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams. Applied Sciences, 9, 5458.

Moayedi H., Tien Bui D., Dounis A., Kok Foong L., Kalantar B., 2019. Novel nature-inspired hybrids of neural computing for estimating soil shear strength. Applied Sciences, 9, 4643.

Mozumder R.A., Laskar A.I., 2015. Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using artificial neural network. Computers and Geotechnics, 69, 291–300.

Narendra B., Sivapullaiah P., Suresh S., Omkar S., 2006. Prediction of unconfined compressive strength of soft grounds using computational intelligence techniques: A comparative study. Computers and Geotechnics, 33, 196–208.

Nguyen H.-L., Le T.-H., Pham C.-T., Le T.-T., Ho L.S., Le V.M., Pham B.T., Ly H.-B., 2019. Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt. Applied Sciences, 9, 3172.

Pham B.T., Hoang T.-A., Nguyen D.-M., Bui D.T., 2018. Prediction of shear strength of soft soil using machine learning methods. Catena, 166, 181–191.

Pham B.T., Le L.M., Le T.-T., Bui K.-T.T., Le V.M., Ly H.-B., Prakash I., 2020a. Development of advanced artificial intelligence models for daily rainfall prediction. Atmospheric Research, 237, 104845.

Pham B.T., Nguyen M.D., Ly H.-B., Pham T.A., Hoang V., Van Le H., Le T.-T., Nguyen H.Q., Bui G.L., 2020b. Development of Artificial Neural Networks for Prediction of Compression Coefficient of Soft Soil, in: Ha-Minh C., Dao D.V., Benboudjema F., Derrible S., Huynh D.V.K., Tang A.M. (Eds.), CIGOS 2019. Innovation for Sustainable Infrastructure, Lecture Notes in Civil Engineering. Springer Singapore, 1167–1172.

Pham B.T., Nguyen-Thoi T., Ly H.-B., Nguyen M.D., Al-Ansari N., Tran V.-Q., Le T.-T., 2020c. Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination. Sustainability, 12, 2339.

Phong T.V., Phan T.T., Prakash I., Singh S.K., Shirzadi A., Chapi K., Ly H.-B., Ho L.S., Quoc N.K., Pham B.T., 2019. Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam. Geocarto International, 1–24.

Pradhan B., Lee S., 2010. Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25, 747–759.

Singh S., 2012. Backpropagation Learning Algorithm Based on Levenberg Marquardt Algorithm, Computer Science & Information Technology.

Spoor G., Godwin R.J., 1979. Soil Deformation and Shear Strength Characteristics of Some Clay Soils at Different Moisture Contents. Journal of Soil Science, 30, 483–498.

Yılmaz I., 2000. Evaluation of shear strength of clayey soils by using their liquidity index. Bulletin of engineering Geology and the Environment, 59, 227–229.


How to Cite

Le, H.-A., Nguyen, T.-A., Nguyen, D.-D., & Prakash, I. (2020). Prediction of soil unconfined compressive strength using Artificial Neural Network Model. Vietnam Journal of Earth Sciences, 42(3), 255–264.

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