Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels
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...
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doaj-705cd78b24e34380b3b1567fba57a8cc2021-04-20T23:01:57ZengMDPI AGApplied Sciences2076-34172021-04-01113705370510.3390/app11083705Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various KernelsJie Zeng0Panayiotis C. Roussis1Ahmed Salih Mohammed2Chrysanthos Maraveas3Seyed Alireza Fatemi4Danial Jahed Armaghani5Panagiotis G. Asteris6Department of Transportation and Municipal Engineering, Chongqing Jianzhu College, Chongqing 400072, ChinaDepartment of Civil and Environmental Engineering, University of Cyprus, Nicosia 1678, CyprusCivil Engineering Department, College of Engineering, University of Sulaimani, Kurdistan Region, Sulaymaniyah 46001, IraqDepartment of Civil Engineering, University of Patras, 26504 Patras, GreeceDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 15875-4413, IranDepartment of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, MalaysiaComputational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Heraklion Athens, GreeceThis 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.https://www.mdpi.com/2076-3417/11/8/3705ground vibrationblasting operationboosting-CHAID: support vector machineinput selection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jie Zeng Panayiotis C. Roussis Ahmed Salih Mohammed Chrysanthos Maraveas Seyed Alireza Fatemi Danial Jahed Armaghani Panagiotis G. Asteris |
spellingShingle |
Jie Zeng Panayiotis C. Roussis Ahmed Salih Mohammed Chrysanthos Maraveas Seyed Alireza Fatemi Danial Jahed Armaghani Panagiotis G. Asteris Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels Applied Sciences ground vibration blasting operation boosting-CHAID: support vector machine input selection |
author_facet |
Jie Zeng Panayiotis C. Roussis Ahmed Salih Mohammed Chrysanthos Maraveas Seyed Alireza Fatemi Danial Jahed Armaghani Panagiotis G. Asteris |
author_sort |
Jie Zeng |
title |
Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels |
title_short |
Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels |
title_full |
Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels |
title_fullStr |
Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels |
title_full_unstemmed |
Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels |
title_sort |
prediction of peak particle velocity caused by blasting through the combinations of boosted-chaid and svm models with various kernels |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-04-01 |
description |
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. |
topic |
ground vibration blasting operation boosting-CHAID: support vector machine input selection |
url |
https://www.mdpi.com/2076-3417/11/8/3705 |
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