Skip to main content

Research Repository

Advanced Search

Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers

Eyo, E. U.; Abbey, S. J.; Lawrence, T. T.; Tetteh, F. K.

Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers Thumbnail


Authors

E. U. Eyo

Profile Image

Samuel Abbey Samuel.Abbey@uwe.ac.uk
Senior Lecturer in Geotechnical Engineering

T. T. Lawrence

F. K. Tetteh



Abstract

Soil swelling-related disaster is considered as one of the most devastating geo-hazards in modern history. Hence, proper determination of a soil's ability to expand is very vital for achieving a secure and safe ground for infrastructures. Accordingly, this study has provided a novel and intelligent approach that enables an improved estimation of swelling by using kernelised machines (Bayesian linear regression (BLR) & bayes point machine (BPM) support vector machine (SVM) and deep-support vector machine (D-SVM)); (multiple linear regressor (REG), logistic regressor (LR) and artificial neural network (ANN)), tree-based algorithms such as decision forest (RDF) & boosted trees (BDT). Also, and for the first time, meta-heuristic classifiers incorporating the techniques of voting (VE) and stacking (SE) were utilised. Different independent scenarios of explanatory features’ combination that influence soil behaviour in swelling were investigated. Preliminary results indicated BLR as possessing the highest amount of deviation from the predictor variable (the actual swell-strain). REG and BLR performed slightly better than ANN while the meta-heuristic learners (VE and SE) produced the best overall performance (greatest R2 value of 0.94 and RMSE of 0.06% exhibited by VE). CEC, plasticity index and moisture content were the features considered to have the highest level of importance. Kernelized binary classifiers (SVM, D-SVM and BPM) gave better accuracy (average accuracy and recall rate of 0.93 and 0.60) compared to ANN, LR and RDF. Sensitivity-driven diagnostic test indicated that the meta-heuristic models’ best performance occurred when ML training was conducted using k-fold validation technique. Finally, it is recommended that the concepts developed herein be deployed during the preliminary phases of a geotechnical or geological site characterisation by using the best performing meta-heuristic models via their background coding resource.

Citation

Eyo, E. U., Abbey, S. J., Lawrence, T. T., & Tetteh, F. K. (2022). Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers. Geoscience Frontiers, 13(1), https://doi.org/10.1016/j.gsf.2021.101296

Journal Article Type Article
Acceptance Date Sep 10, 2021
Online Publication Date Sep 13, 2021
Publication Date 2022-01
Deposit Date Sep 14, 2021
Publicly Available Date Oct 21, 2021
Journal Geoscience Frontiers
Print ISSN 1674-9871
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 13
Issue 1
Article Number 101296
DOI https://doi.org/10.1016/j.gsf.2021.101296
Keywords General Earth and Planetary Sciences
Public URL https://uwe-repository.worktribe.com/output/7767460

Files





You might also like



Downloadable Citations