Abstract
This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.
Highlights
IntroductionBlasting is a usual method of breakage in mining and quarrying processes
The results showed that artificial neural network (ANN) outperformed other models
The results showed that BC-SVMSIG had the worst gain for both the training and testing phases
Summary
Blasting is a usual method of breakage in mining and quarrying processes. It is one of the standard techniques used in several projects such as road and tunnel construction [1]. Despite the availability of several experimental analytic solutions for predicting these environmental effects, these specifications take into account only a small number of important factors, whereas other influential parameters such as the blasting pattern and geological circumstances influence these impacts as well [15,16]. Experimental methods are not precise enough, while in some cases, predicting the environmental effect with greater certainty is crucial for reducing ecological harm due to blasting [17]
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