Iranian Journal of Geophysics

Iranian Journal of Geophysics

Comparison of various machine learning algorithms for estimation of uniaxial compressive strength of cement and volcanic ash stabilized clay

Document Type : Research Article

Authors
Khadije Mahmoodi
Hajar Momeni
Assistant Professor, Department of Civil Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran
10.30499/ijg.2023.408085.1530
Abstract
One of the challenges in construction on poor clay soils is their low compressive strength and high settlement. Therefore, geotechnical engineers are always trying to improve the characteristics of this type of soil using different soil stabilization methods. Estimation of the efficiency of different soil stabilization methods requires numerous experimental tests, which can be time-consuming and costly. This article examines the performance of various machine learning methods, including linear regression (LR), multilayer perceptron neural network (MLP), K-nearest neighbor, support vector machine regressor (SVR), decision tree, random forest, and gradient boosting machine in estimating the unconfined compressive strength of stabilized clay soil using two types of pozzolans under different curing conditions. The input variables for the machine learning algorithms include time and curing conditions, the molarity of the alkaline activator, the binder replacement, the type of pozzolan, and ratio of the activator to the optimal moisture content. To optimize and speed up the performance of the algorithms, hyperparameter tuning was performed using a grid search method. In addition, to reduce the variability of the results with respect to data division into training and testing sets, a 10-fold cross-validation method was used. The performance of the algorithms was evaluated using three criteria, namely mean square error, coefficient of determination, and mean absolute percentage error. Given the very close results of the SVR method to the MLP method, these algorithms can be confidently recommended for the estimation of UCS of the studied soil. Moreover, based on the performance evaluation criteria, the linear regression method has the worst results among the models used on the experimental data. Also, the sensitivity analysis of the models shows that, in order of preference, the input variables of binder replacement, molarity and curing time have the highest effect on the predicted compressive strength. The results of parametric analysis represent that by increasing the curing time, for both types of binders, namely cement and volcanic ash, uniaxial compressive strength increases. In addition, by increasing the percentage of binders under the optimum water condition, the uniaxial strength of soil increases, while in dry condition with increasing the binder replacement, for volcanic ash stabilized soil, the UCS decreases and for cement stabilized soil it fluctuates. It is observed that the compressive strength has a direct relationship with the amount of molarity. The results indicate also that by increasing the alkali activator/optimum water content up to 1.2, for the curing time of 28 days, the compressive strength increases and then it will be constant.
Keywords
Volcanic ash
clay
cement
uniaxial compressive strength
machine learning
Subjects
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Iranian Journal of Geophysics
Volume 18, Issue 2
July and August 2024
Pages 19-37

  • Receive Date 22 July 2023
  • Revise Date 18 October 2023
  • Accept Date 23 October 2023
  • First Publish Date 23 October 2023
  • Publish Date 21 June 2024