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Open AccessArticle

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms

1
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
2
Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Korea
3
Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Pho Vien, Duc Thang ward, Bac Tu Liem district, Hanoi 100000, Vietnam
4
Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Pho Vien, Duc Thang ward, Bac Tu Liem district, Hanoi 100000, Vietnam
5
Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
6
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(1), 132; https://doi.org/10.3390/s20010132
Received: 4 November 2019 / Revised: 16 December 2019 / Accepted: 20 December 2019 / Published: 24 December 2019
In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms were used to optimize the support vector regression (SVR) model. They were abbreviated as the PSO-SVR, GA-SVR, ICA-SVR, and ABC-SVR models. For each evolutionary algorithm, three forms of kernel function, linear (L), radial basis function (RBF), and polynomial (P), were investigated and developed. In total, 12 new hybrid models were developed for predicting PPV in this study, named ABC-SVR-P, ABC-SVR-L, ABC-SVR-RBF, PSO-SVR-P, PSO-SVR-L, PSO-SVR-RBF, ICA-SVR-P, ICA-SVR-L, ICA-SVR-RBF, GA-SVR-P, GA-SVR-L and GA-SVR-RBF. There were 125 blasting results gathered and analyzed at a limestone quarry in Vietnam. Statistical criteria like R2, RMSE, and MAE were used to compare and evaluate the developed models. Ranking and color intensity methods were also applied to enable a more complete evaluation. The results revealed that GA was the most dominant evolutionary algorithm for the current problem when combined with the SVR model. The RBF was confirmed as the best kernel function for the GA-SVR model. The GA-SVR-RBF model was proposed as the best technique for PPV estimation. View Full-Text
Keywords: peak particle velocity; vibration sensor; soft computing; evolutionary algorithm; hybrid model; open-pit mine peak particle velocity; vibration sensor; soft computing; evolutionary algorithm; hybrid model; open-pit mine
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Nguyen, H.; Choi, Y.; Bui, X.-N.; Nguyen-Thoi, T. Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms. Sensors 2020, 20, 132.

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