Consolidation Coefficient of Soil Prediction by Using Teaching Learning based Optimization with Fuzzy Neural Network


  • K Kalaivani Coimbatore Institute of Technology
  • D Mohana Priya Sri Eshwar College of Engineering image/svg+xml
  • K Veena Akshaya College of Engineering
  • K Brindha RVS College of Engineering and Technology
  • K Karuppasamy RVS College of Engineering and Technology
  • K R Shanmugapriyaa Coimbatore Institute of Technology



Machine learning, Fuzzy neural network, Teaching optimization based on learning, Consolidation coefficient, Feature selection


A key factor in constructing buildings leaning on soft soil is the consolidating coefficient of the soil referred as Cv. It is a crucial lab-measured engineering parameter utilized during the design and verification of geotechnical structures. Nevertheless, experimental experiments take a lot of time and money. In this study, the   is projected using Fuzzy Neural Network (FNN) with optimized feature selection using Teaching Learning-based Optimization, estimating Cv as the most crucial step (TLO), which has enhanced the quality of the prediction model by removing unnecessary characteristics and relying solely on crucial ones. The experimental results demonstrate that the projected FNN, followed by the Multi-layer Training algorithm Neural Network (MLP), Impact of changing Optimization (BBO), a support vector regression (SVR), Back - propagation algorithm Multi-layer Training algorithm Bayesian Network (Bp-MLP Neural Nets), has the highest predictive validity for the prediction of   (Root Mean Squared Error (RMSE )= 0.379, Mean Absolute Error (MAE) = 0.26, and coefficient of determination r = 0.835). Hence, it can be said that even if all used models perform well in predicting the soil consolidation coefficient, the FNN-TLO performs the best.


Download data is not yet available.
<br data-mce-bogus="1"> <br data-mce-bogus="1">


Narasimha P, Pandian N, Nagaraj T. Analysis and estimation of the coefficient of consolidation. Geotechnical Testing Journal .1995;Vol. 18 (2):pp. 252–258. DOI:

Shahin M A, Jaksa M B, Maier H R. Recent advances and future challenges for artificial neural systems in geotechnical engineering applications. Advanced Artificial Neural System.2009; pp. 5. DOI:

Schnaid F, Sills G C, Soares, Nyirenda Z. Predictions of the coefficient of consolidation from piezocone tests. Canadian Geotechnology Journal. 1997; Vol. 34 (2):pp. 315–327. DOI:

Sivakugan N, Eckersley J, Li H. Settlement predictions using neural networks. Australian Civil Engineering Transaction. 1998; Vol. 40: pp. 49–52.

Ryohei I, Noriyuki Y, Michael J. An estimation method for predicting final consolidation settlement of ground improved by floating soil cement columns. Soils Foundation. 2016; Vol. 56 (2): pp. 213–227. DOI:

Chen W M, Panahi M, Pourghasemi H R. 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.2017; pp. 310–324. DOI:

Gooroochurn M, Kerr D, Bouazza-Marouf K. A machine learning approach to tracking and characterizing planar or near planar fluid flow, International Journal Natural Computer Research (IJNCR).2020; Vol. 9 (3):pp. 76–87. DOI:

Johari A, Javadi A, Habibagahi G.: Modelling the mechanical behavior of unsaturated soils using a genetic algorithm-based neural network. Computer Geotechnics. 2011; Vol. 38 (1):pp. 2–13. DOI:

Kaya A. Residual and fully softened strength evaluation of soils using artificial neural networks. Geotechnical Geological Engineering.2009; Vol. 27 (2):pp. 281–288. DOI:

Pham B T, Son L H, Hoang T A, Nguyen D M, Bui D T. Prediction of shear strength of soft soil using machine learning methods. CATENA.2018; Vol. 166: pp. 181–191. DOI:

Thai B, Sushant K, Hai-Bang Ly. Using Artificial Neural Network for prediction of soil coefficient of consolidation. Vietnam Journal Earth Science. 2020; Vol.42 (4). DOI:

Manh Duc Nguyen, Binh Thai Pham, Lanh Si Ho.: Soft-computing techniques for prediction of soils consolidation coefficient, CATENA. Vol. 195 (2020). DOI:

Sathya Ramasamy, Ananthi Selvarajan, Vaidehi Kaliyaperumal, Prasanth Aruchamy. A hybrid location-dependent ultra-convolutional neural network-based vehicle number plate recognition approach for intelligent transportation systems. Concurrency and Computation. Practice and Experience. 2023; Vol. 35(1). DOI:

Verleysen M, François D. The curse of dimensionality in data mining and time series prediction. Computational Intelligence and Bioinspired Systems, Springer Berlin Heidelberg, Berlin, Heidelberg, 2005; pp. 758–770. DOI:

Sridharan A, Nagaraj H. Coefficient of consolidation and its correlation with index properties of remolded soils. Geotech. Test. Journal.2004; Vol.27: pp.469–474. DOI:

Chen W. Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China Science Total Environmental. 2018; Vol. 626: pp. 1121–1135. DOI:

Chen W. A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomatics Natural Hazard Risk. 2018; Vol.8: pp. 1955–1977. DOI:

Chen W. GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena.2018; Vol.164: pp. 135–149. DOI:

Chen W.: GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve Bayes tree, and alternating decision tree models Geomatics Natural Hazard Risk. 2017; Vol.8: pp. 950–973. DOI:

Pham B T, Prakash I. Evaluation and comparison of LogitBoost Ensemble, Fisher's Linear Discriminant Analysis, logistic regression, and support vector machines methods for landslide susceptibility mapping. Geocarto International. 2017; pp.1–32. DOI:

Qi C, Tang X. Slope stability prediction using integrated metaheuristic and machine learning approaches: a comparative study. Computer International Engineering. 2018; Vol. 118: pp.112–122. DOI:

Shahabi H, Hashim M. Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Scientific Reports.2015; Vol.5 . DOI:

Shadman Roodposhti M, Aryal J, Shahabi H, Safarrad T. Fuzzy Shannon entropy: a hybrid GIS-based landslide susceptibility mapping method. Entropy. 2016; Vol. 18: pp. 343. DOI:

Kanungo D P, Sharma S, Pain A. Artificial neural network and regression tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters. Frontiers of Earth Science. 2014; Vol. 8: pp. 439–456. DOI:

Niranjani V, Selvam N S. Overview on Deep Neural Networks: Architecture, Application and Rising Analysis Trends. EAI/Springer Innovations in Communication and Computing. 2020; pp. 271–278. DOI:

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

Erzin Y, Ecemis N. The use of neural networks for the prediction of cone penetration resistance of silty sands. Neural Computing. & Application. 2017; Vol. 28: pp.727–736. DOI:

Javdanian H, Lee S. Evaluating unconfined compressive strength of cohesive soils stabilized with geopolymer: a computational intelligence approach. Engineering with Computers. pp. 1–9.

Pham B T, Nguyen M D, Bui K. A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil. Catena. 2019; Vol. 173: pp. 302-311. DOI:

Mittal, Mamta, Suresh Chandra Satapathy. Prediction of coefficient of consolidation in soil using machine learning techniques. Microprocessors and Microsystems. 2021; Vol.82. DOI:

Das B M, Sobhan K. Principles of Geotechnical Engineering. Cengage learning. 2013.

Spagnoli G, Feinendegen M.: Relationship between measured plastic boundary and plastic boundary estimated from undrained shear strength, water content ratio and liquidity index. Clay Miner. 2017: Vol.52: pp. 509–519. DOI:

Kihara E N, Gichangi P, Liversidge H M, Butt F.: Dental age estimation in a group of Kenyan children using Willems’ method: a radiographic study. Annals of Human Biology. 2017; pp. 1-8. DOI:

Crespinsek M, Liu S H, Mernik L. A note on teaching-learning-based optimization algorithm. Information Sciences. 2012; Vol. 212: pp. 79-93. DOI:

Rao R V, Savsani V J, Vakharia D P. Teaching-learning-based optimization: An optimization method for continuous nonlinear large scale problems. Information Sciences. 2012; Vol. 183: pp. 1-15. DOI:

Gabrys B, Burgiela A. General fuzzy min-max neural network for clustering and classification. IEEE Transactions Neural Networks. 2000; Vol. 11(3): pp. 769-783. DOI:

Lee H M, Chen C M, Jou Y L: An efficient fuzzy classifier with feature selection based on fuzzy entropy. IEEE Transactions SMC-B. 2001; Vol. 31(3): pp. 426-432. DOI:




How to Cite

K. Kalaivani, D. Mohana Priya, K. Veena, K. Brindha, K. Karuppasamy, and K. R. Shanmugapriyaa, “Consolidation Coefficient of Soil Prediction by Using Teaching Learning based Optimization with Fuzzy Neural Network”, EAI Endorsed Trans IoT, vol. 10, Jan. 2024.