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

Nguyen, Hoang; Choi, Yosoon; Bui, Xuan‐Nam; Nguyen-Thoi, T.

doi:10.3390/s20010132

Cited by 71 publications

(22 citation statements)

References 63 publications

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“…It is problem-dependent. It is also very challenging because the hyperparameters must be selected from continuous domains, where there is an infinite number of possible choices [33,[89][90][91][92].…”

Section: E History-based Adaptive Differential Evolution With Linear Population Size Reduction and Population-wide Inertia (L-shade-pwi)mentioning

confidence: 99%

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Dinh

Nguyen

Tran

et al. 2021

Mathematical Problems in Engineering

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Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

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Section: E History-based Adaptive Differential Evolution With Linear Population Size Reduction and Population-wide Inertia (L-shade-pwi)mentioning

confidence: 99%

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Dinh

Nguyen

Tran

et al. 2021

Mathematical Problems in Engineering

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“…at algorithm is developed from the statistical learning theory, and the input variables were reflected into a high-dimensional space by using the kennel function [65,66]. e kennel function provided by support vector regression (SVR) mainly includes three types such as the linear kernel, polynomial kernel, and radial primary kernel function [67]. Among these kernel functions, the radial primary kernel function was found to be the most efficient kernel function for higher predictive performance [68].…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

Jiang¹,

Xu²,

Chen

et al. 2021

Advances in Civil Engineering

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The accurate determination of blast-induced ground vibration has an important significance in protecting human activities and the surrounding environment. For evaluating the peak particle velocity resulting from the quarry blast, a robust artificial intelligence system combined with the salp swarm algorithm (SSA) and Gaussian process (GP) was proposed, and the SSA was used to find the optimal hyperparameters of the GP here. In this regard, 88 datasets with 9 variables including the ratio of bench height to burden (H/B) and the ratio of spacing to burden (S/B) were selected as the input variables, while peak particle velocity (PPV) was measured. Then, an ANN model, an SVR model, a GP model, an SSA-GP model, and three empirical models were established, and the predictive performance was evaluated by using the root-mean-square error (RMSE), determination coefficient (R2), value account for (VAF), Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and the run time. After comparing, it is found that the proposed SSA-GP yielded a superior performance and the ratio of bench height to burden (H/B) was the most sensitive variable.

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“…In Malaysia, Armaghani et al [1], Hajihassani et al [17], and Shirani Faradonbeh et al [72] used particle swarm optimization (PSO)-ANN, imperialism competitive algorithm (ICA)-ANN and gene expression programming (GEP) techniques, respectively, to estimate ground vibrations resulting from blasting. In Vietnam, Nguyen et al [73,74] respectively developed two new models namely the K-means clustering (HKM)-Cubist algorithm (CA), and the support vector regression (SVR)-genetic algorithm (GA) for PPV prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

Zeng¹,

Roussis

Mohammed

et al. 2021

Applied Sciences

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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.

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

Cited by 71 publications

References 63 publications

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

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