Prediction of coefficient of consolidation in soil using machine learning techniques

Abstract Coefficient of consolidation in the soil is the significant engineering properties and an important parameter for designing and auditing of geo-technical structures. Therefore, in this study, authors have proposed an efficient methodology to prediction the coefficient of consolidation using machine learning models namely Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Adaptive Network based Fuzzy Inference System (ANFIS). Further, various feature selection techniques such as Least Absolute Shrinkage and Selection Operator algorithm (LASSO), Random Forests - Recursive Feature Elimination (RF-RFE), and Mutual information have also been applied. It has been observed that feature selection methods have enhanced the quality of prediction model by eliminating the irrelevant features and utilized only important features while building the prediction models. Experiments are performed on the dataset collected on the 534 soil samples from Ha Noi –Hai Phong highway project, Vietnam. Experimental results show the adequacy of the proposed model, and the hybrid approach ANFIS which is a fusion of ANN and fuzzy inference system includes complementary information of the uncertainty and adaptability. ANFIS along with LASSO feature selection method produces the coefficient of determination of 0.831 and thus provides the best prediction for the coefficient of consolidation of a soil as compared to other approaches.

[1]  N. Sivakugan,et al.  Settlement Predictions Using Neural Networks , 1998 .

[2]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[3]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[4]  R. Lippmann.  Pattern classification using neural networks , 1989, IEEE Communications Magazine.

[5]  Mehdi Vafakhah,et al.  A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods , 2013, Water Resources Management.

[6]  Dieu Tien Bui,et al.  Soft-computing techniques for prediction of soils consolidation coefficient , 2020 .

[7]  A. Kaya.  Residual and Fully Softened Strength Evaluation of Soils using Artificial Neural Networks , 2009 .

[8]  C. Furlanello,et al.  Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products , 2006 .

[9]  P. N. Raju,et al.  Analysis and Estimation of the Coefficient of Consolidation , 1995 .

[10]  Michel Verleysen,et al.  The Curse of Dimensionality in Data Mining and Time Series Prediction , 2005, IWANN.

[11]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Ryohei Ishikura,et al.  An estimation method for predicting final consolidation settlement of ground improved by floating soil cement columns , 2016 .

[13]  B. Das,et al.  Methods for determination of the coefficient of consolidation and field observations of time rate of settlement — an overview , 2009 .

[14]  David Kerr,et al.  A Machine Learning Approach to Tracking and Characterizing Planar or Near Planar Fluid Flow , 2020, Int. J. Nat. Comput. Res..

[15]  G. Sills,et al.  Predictions of the coefficient of consolidation from piezocone tests , 1997 .

[16]  D. Basak,et al.  Support Vector Regression , 2008 .

[17]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[18]  Sami Ekici,et al.  An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM , 2009, Expert Syst. Appl..

[19]  Thai Binh Pham,et al.  Using Artificial Neural Network (ANN) for prediction of soil coefficient of consolidation , 2020 .

[20]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[21]  Brian C. Ross.  Mutual Information between Discrete and Continuous Data Sets , 2014, PloS one.

[22]  Suk-Tae Seo,et al.  Support Vector Regression-based Model to Analyze Prognosis of Infants with Congenital Muscular Torticollis , 2010, Healthcare informatics research.

[23]  Akbar A. Javadi,et al.  Modelling the mechanical behaviour of unsaturated soils using a genetic algorithm-based neural network , 2011 .

[24]  Binh Thai Pham,et al.  Prediction of shear strength of soft soil using machine learning methods , 2018, CATENA.

[25]  Braja M. Das,et al.  Principles of Geotechnical Engineering , 2021 .

[26]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[27]  Ahmed El-Shafie,et al.  Water quality prediction model utilizing integrated wavelet-ANFIS model with cross-validation , 2010, Neural Computing and Applications.

[28]  Athanasios Sfetsos,et al.  A comparison of various forecasting techniques applied to mean hourly wind speed time series , 2000 .

[29]  Holger R. Maier,et al.  Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications , 2009, Adv. Artif. Neural Syst..

[30]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[31]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[32]  Hamid Reza Pourghasemi,et al.  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 , 2017 .

[33]  K. Terzaghi,et al.  Soil mechanics in engineering practice , 1948 .