Modeling compaction parameters using support vector and decision tree regression algorithms

Abstract

Shortening the periods of compaction tests can be possible by analyzing the data obtained from previous laboratory tests with regression methods. The regression analysis applied to current data reduces the cost of experiments, saves time, and gives estimated outputs. In this study, the MLS-SVR, KB-SVR, and DTR algorithms were employed for the first time for the estimation of soil compaction parameters. The performances of these regression algorithms in estimating maximum dry unit weight (MDD) and optimum water content (OMC) were compared. Furthermore, the soil properties (fine-grained soil, sand, gravel, specific gravity, liquid limit, and plastic limit) were employed as inputs in the study. The data used for the study were supplied from the experimental soil tests from small dams in Niğde, a province in the southern part of Central Anatolia, Turkey. Polynomial-based KB-SVR yielded the best R-values with 0.93 in the prediction of both OMC and MDD. Moreover, in the multioutput estimation model, polynomial and RBFbased KB-SVR methods were successful with 0.98 and 0.99, respectively. Additionally, while the MSE value was 1.33 in the estimation of OMC, this value was 0.04 in the estimation of MDD. Accordingly, MDD was the most successfully estimated parameter in all processes. It was concluded that through the algorithms used in this study, the prediction of soil compaction parameters could be possible without the need for further laboratory tests.

Deep Neural Network Models for the Prediction of the Aggregate Base Course Compaction Parameters

“…This was followed by two studies [26,27] that utilize MLR models for the prediction of the two compaction parameters. Özbeyaz and Soylemez (2020) used two approaches which are the regression analysis and supporting vector machine for predicting the OMC and MDD using the grain size distribution, specific gravity, liquid limit, and plastic limit as inputs [28]. From the previous discussion, it is clear that machine learning approaches outperform the traditional regression models; however, rare studies employed this approach for estimating the two compaction parameters of the aggregate base course.…”

“…Similarly, Othman [31] utilized ANNs to develop prediction models that can be used for predicting the characteristics of the hot asphalt mixes. Additionally, ANNs have been used in a number of studies for the prediction of the soil properties such as the study by Ardakani and Kordnaeij (2017) [25], the study by Sinha and Wang (2006) [16], the study by Özbeyaz, and Soylemez (2020) [28], and the study by Othman and Abdelwhab [32]. In general, ANN is a system that tries to mimic the human brain system or the neural system.…”

“…Figure 3 shows the proposed general ANN structure followed in this paper. The number of hidden layer and neurons per hidden layers used in this study are based on the architectures of ANNs that were tested in previous studies as the maximum number of layers tested in the previous studies is four, and the maximum number of neurons employed in previous studies is 20 neurons per hidden layer [3,16,25,28,31]. Additionally, the weights of the connections are randomly assigned before launching the training process.…”

Modelling the Effects of Nanomaterial Addition on the Permeability of the Compacted Clay Soil Using Machine Learning-Based Flow Resistance Analysis

“…In Ozbeyaz and Soylemez's study, Support Vector Machine and decision trees are used to calculate compaction parameters. The Support Vector Machine model is found to produce better results [34]. On the other hand, Singh et al hypothesize that Support Vector Machines, Multi Linear Regression, Genetic Programming, and Random Forest algorithms are used for permeability estimation.…”