A new soft computing model for estimating and controlling blast-produced ground vibration based on Hierarchical K-means clustering and Cubist algorithms

Nguyen, Hoang; Bui, Xuan‐Nam; Tran, Quang-Hieu; Mai, Ngoc-Luan

doi:10.1016/j.asoc.2019.01.042

Cited by 122 publications

(34 citation statements)

References 28 publications

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“…As a result, the ICA quadratic form is the most dominant model among the developed models with an RMSE of 0.37 and R 2 of 0.94. Several similar works can be found at those references [51][52][53][54][55].…”

Section: Introductionmentioning

confidence: 79%

Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam

Nguyen

2019

SN Appl. Sci.

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Blast-induced ground vibration (PPV) is one of the effects of the hazard in open-pit mines. It can make vibration of the structure, instability of slope and bench, impact on underground water, and the surrounding residential. Therefore, the precise prediction of blast-induced PPV is needed to minimize undesirable effects on the surrounding environment. Also, one of the most important objectives of this study is to determine the site characteristics for the application of controlled blasting techniques. In this study, the support vector regression (SVR) approach was considered and developed for predicting blast-induced PPV in an open-pit coal mine (Vietnam) as a case study. Three forms of the equation include linear (L), polynomial (P), and radius basis function (RBF) were applied to develop the SVR models. For comparison purpose, an empirical technique was also referred to as estimate blast-induced PPV in this study on the same training dataset. A database with 181 blasting events was used for this aim. Performance indicators such as root-mean-square error (RMSE) and the coefficient of determination (R 2 ) were used to compare and evaluate the performance of the predictive models. The results showed that SVR was an effective approach for predicting blast-induced PPV in this study. Among three forms of the equation, the SVR model with RBF was the most superior model for predicting blast-induced PPV in this study with an RMSE of 0.396, R 2 of 0.924, and MAE of 0.135, whereas the empirical model only obtained performance with an RMSE of 0.856, R 2 of 0.643, and MAE of 0.575. This study provided an overview of the SVR approach in predicting blast-induced PPV. A comparison of different kernel functions for the selection of the SVR model is needed to find out the best model for predicting blast-induced PPV.

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Section: Introductionmentioning

confidence: 79%

Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam

Nguyen

2019

SN Appl. Sci.

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“…Behzadafshar et al [44] ICA-linear R 2 = 0.939; RMSE = 0.320; VAF = 92.18%; MBE = 0.22; MAPE = 0.038 Tian et al [45] GA-power R 2 = 0.977; RMSE = 0.285 Hasanipanah et al [46] FS-ICA R 2 = 0.942; RMSE = 0.22; VAF = 94.2% Nguyen et al [12] HKM-ANN R 2 = 0.983; RMSE = 0.554; VAF = 97.488% Nguyen et al [11] HKM-CA R 2 = 0.995; RMSE = 0.475; MAE = 0.373 Zhang et al [8] PSO-XGBoost R 2 = 0.968; RMSE = 0.583; MAE = 0.346, VAF = 96.083…”

Section: Reference Methods Resultsmentioning

confidence: 99%

“…In this regard, artificial intelligence (AI) applications are considered useful, not only as robust techniques in the mining field but also in many other areas (e.g., civil engineering, fuel, and energy, and environment) [2,4,[6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. An overview of the literature related to PPV prediction showed that many AI models have been developed and proposed, as listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen

Choi

Bui

et al. 2019

Sensors

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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.

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“…In order to develop the ilmenite content prediction models, the dataset was separated into two groups according to previous studies [60,78,79]. Specifically, 80% of the original dataset (325 samples) was used to train the models, called the training dataset; 20% remaining of the data (80 samples) was used to validate the model, called the testing dataset.…”

Section: Development Of the Modelmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Bui

et al. 2020

Applied Sciences

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In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

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A new soft computing model for estimating and controlling blast-produced ground vibration based on Hierarchical K-means clustering and Cubist algorithms

Cited by 122 publications

References 28 publications

Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam

Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam

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

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

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