Estimation of shear strength parameters of soil using Optimized Inference Intelligence System

Binh Thai Pham, Mahdis Amiri, Manh Duc Nguyen, Trinh Quoc Ngo, Kien Trung Nguyen, Hieu Trung Tran, Hoanng Vu, Bui Thi Quynh Anh, Hiep Van Le, Indra Prakash


In recent years, machine learning techniques have been developed and used to build intelligent information systems for solving problems in various fields. In this study, we have used Optimized Inference Intelligence System namely ANFIS-PSO which is a combination of Adaptive Neural-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) for the estimation of shear strength parameters of the soils (Cohesion “C” and angle of internal friction “φ”). These parameters are required for designing the foundation of civil engineering structures. Normally, shear parameters of soil are determined either in the field or in the laboratory which require time, expertise and equipments. Therefore, in this study, we have applied a hybrid model ANFIS-PSO for quick and cost-effective estimation of shear parameters of soil based on the other six physical parameters namely clay content, natural water content, specific gravity, void ratio, liquid limit and plastic limit. In the model study, we have used data of 1252 soft soil samples collected from the different highway project sites of Vietnam. The data was randomly divided into 70:30 ratios for the model training and testing, respectively. Standard statistical measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Correlation Coefficient (R) were used for the performance evaluation of the model. Results of the model study indicated that performance of the ANFIS-PSO model is very good in predicting shear parameters of the soil: cohesion (RMSE = 0.075, MAE = 0.041, and R = 0.831) and angle of internal friction (RMSE = 0.08, MAE = 0.058, and R = 0.952).


Adaptive Neural-Fuzzy inference system; particle swarm optimization; shear strength; soft soil; Vietnam

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Amaro J., Rosado D.J.M., Mendiburu A.Z., dos Santos L.R., de Carvalho Jr J.A., 2021. Modeling of syngas composition obtained from fixed bed gasifiers using Kuhn-Tucker multipliers. Fuel, 287, 119068.

Armaghani D.J., Tonnizam Mohamad E., Momeni E., Monjezi M., Sundaram Narayanasamy M., 2015. Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arabian Journal of Geosciences, 9, 48.

Besalatpour A., Hajabbasi M., Ayoubi S., Afyuni M., Jalalian A., Schulin R., 2012. Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system. Soil science and plant nutrition, 58, 149-160.

Breiman L., 2001. Random Forests. Machine Learning, 45, 5-32.

Bui D.T., Hoang N.-D., Nhu V.-H., 2019. A swarm intelligence-based machine learning approach for predicting soil shear strength for road construction: a case study at Trung Luong National Expressway Project (Vietnam). Engineering with Computers, 35, 955-965.

Chen W., Panahi M., Pourghasemi H.R., 2017. Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA, 157, 310-324.

Chen W., Wang Y., Cao G., Chen G., Gu Q., 2014. A random forest model based classification scheme for neonatal amplitude-integrated EEG. Biomed Eng Online, 13 Suppl 2, S4-S4.

Cockshott A.R., Hartman B.E., 2001. Improving the fermentation medium for Echinocandin B production part II: Particle swarm optimization. Process Biochemistry, 36, 661-669.

Das B.M., 2021. Principles of geotechnical engineering. Cengage learning.

Eberhart R., Kennedy J., 1995. A new optimizer using particle swarm theory, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Publishing, 39-43.

Foong L.K., Moayedi H., Lyu Z., 2020. Computational modification of neural systems using a novel stochastic search scheme, namely evaporation rate-based water cycle algorithm: an application in geotechnical issues. Engineering with Computers.

Ghanizadeh A.R., Tavana Amlashi A., 2018. Prediction of fine-grained soils resilient modulus using hybrid ANN-PSO, SVM-PSO and ANFIS-PSO methods. Quarterly Journal of Transportation Engineering, 9, 159-181.

Guo X., Ji M., Zhao Z., Wen D., Zhang W., 2020. Global path planning and multi-objective path control for unmanned surface vehicle based on modified particle swarm optimization (PSO) algorithm. Ocean Engineering, 216, 107693.

Jaypuria S., Ranjan Mahapatra T., Jaypuria O., 2019. Metaheuristic Tuned ANFIS Model for Input-Output Modeling of Friction Stir Welding. Materials Today: Proceedings, 18, 3922-3930.

Kalatehjari R., Ali N., Kholghifard M., Hajihassani M., 2014. The effects of method of generating circular slip surfaces on determining the critical slip surface by particle swarm optimization. Arabian Journal of Geosciences, 7, 1529-1539.

Kanungo D.P., Sharma S., Pain A., 2014. Artificial Neural Network (ANN) and Regression Tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters. Frontiers of Earth Science, 8, 439-456.

Kiran S., Lal B., Tripathy S., 2016. Shear strength prediction of soil based on probabilistic neural network. Indian Journal of Science and Technology, 9.

Kuo Y.L., Jaksa M.B., Lyamin A.V., Kaggwa W.S., 2009. ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil. Computers and Geotechnics, 36, 503-516.

Li J., Heap A.D., 2014. Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling & Software, 53, 173-189.

Ly H.-B., Pham B.T., 2020. Prediction of shear strength of soil using direct shear test and support vector machine model. The Open Construction and Building Technology Journal, 14.

Nguyen Q.H., Ly H.-B., Ho L.S., Al-Ansari N., Le H.V., Tran V.Q., Prakash I., Pham B.T., 2021. Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil. Mathematical Problems in Engineering.

Nhu V.-H., Hoang N.-D., Duong V.-B., Vu H.-D., Bui D.T., 2020. A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam). Engineering with Computers, 36, 603-616.

Noushabadi A.S., Dashti A., Raji M., Zarei A., Mohammadi A.H., 2020. Estimation of cetane numbers of biodiesel and diesel oils using regression and PSO-ANFIS models. Renewable Energy, 158, 465-473.

Nwobi-Okoye C.C., Ochieze B.Q., Okiy S., 2019. Multi-objective optimization and modeling of age hardening process using ANN, ANFIS and genetic algorithm: Results from aluminum alloy A356/cow horn particulate composite. Journal of Materials Research and Technology, 8, 3054-3075.

Panem C., Gad V.R., Gad R.S., 2020. Sensor’s data transmission with BPSK using LDPC (Min-Sum) error corrections over MIMO channel: Analysis over RMSE and BER. Materials Today: Proceedings, 27, 571-575.

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., Nguyen-Thoi T., Ly H.-B., Nguyen M.D., Al-Ansari N., Tran V.-Q., Le T.-T., 2020. Extreme learning machine based prediction of soil shear strength: a sensitivity analysis using Monte Carlo simulations and feature backward elimination. Sustainability, 12, 2339.

Qasim M., Park S., Kim J.-O., 2020. Empirical Reynolds number model for drag coefficient determination of ballasted flocs. Journal of Water Process Engineering, 101803.

Salehin S., 2017. Investigation into engineering parameters of marls from Seydoon dam in Iran. Journal of Rock Mechanics and Geotechnical Engineering, 9, 912-923.

Samui P., Hoang N.-D., Nhu V.-H., Nguyen M.-L., Ngo P.T.T., Bui D.T., 2019. A New Approach of Hybrid Bee Colony Optimized Neural Computing to Estimate the Soil Compression Coefficient for a Housing Construction Project. Applied Sciences, 9, 4912.

Sharma L.K., Singh R., Umrao R.K., Sharma K.M., Singh T.N., 2017. Evaluating the modulus of elasticity of soil using soft computing system. Eng. with Comput., 33, 497-507.

Tan H., Chen F., Chen J., Gao Y., 2019. Direct shear tests of shear strength of soils reinforced by geomats and plant roots. Geotextiles and Geomembranes, 47, 780-791.

Walia N., Singh H., Sharma A., 2015. ANFIS: Adaptive neuro-fuzzy inference system-a survey. International Journal of Computer Applications, 123.

Wang X., Ting D.S.K., Henshaw P., 2020. Mutation particle swarm optimization (M-PSO) of a thermoelectric generator in a multi-variable space. Energy Conversion and Management, 224, 113387.

Zhang Z., Peng B., Luo C.-H., Tai C.-C., 2021. ANFIS-GA system for three-dimensional pulse image of normal and string-like pulse in Chinese medicine using an improved contour analysis method. European Journal of Integrative Medicine, 101301.

Zhou W., Dong H., Liang Y., 2020. The deterministic dendritic cell algorithm with Haskell in earthquake magnitude prediction. Earth Science Informatics, 13, 447-457.



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