Prediction of blast-produced ground vibration using particle swarm optimization

Hasanipanah, Mahdi; Naderi, Reyhaneh; Kashir, Javad; Noorani, Seyed Ahmad; Qaleh, Ali Zeynali Aaq

doi:10.1007/s00366-016-0462-1

Cited by 124 publications

(36 citation statements)

References 40 publications

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“…Khandelwal, Singh [34] ANN R 2 = 0.986; MAE = 0.196 Khandelwal et al [35] SVM R 2 = 0.955; MAE = 0.226 Saadat et al [36] ANN-LM R 2 = 0.957; MSE = 0.000722 Hajihassani et al [37] ICA-ANN R 2 = 0.976 Hajihassani et al [38] PSO-ANN R 2 = 0.89; MSE = 0.038 Amiri et al [39] ANN-KNN R 2 = 0.88; RMSE = 0.54; VAF = 87.84 Hasanipanah et al [40] CART R 2 = 0.95; RMSE = 0.17; NS = 0.948 Hasanipanah et al [41] PSO-power R 2 = 0.938; RMSE = 0.24; VARE = 0.13; NS = 0.94 Taheri et al [42] ABC-ANN R 2 = 0.92; RMSE = 0.22; MAPE = 4.26 Faradonbeh, Monjezi [43] GEP-COA R 2 = 0.874; RMSE = 6.732; MAE = 5.164…”

Section: Reference Methods Resultsmentioning

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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Section: Reference Methods Resultsmentioning

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 section, an RF model and a HHO-RF model were developed to establish a stable and accurate relationship between the input variables (f, B, T, D V , D H , Q total , Q max ) and output variable (PPV). To evaluate the predictive performance, three performance indices including RMSE, R 2 and MAE, were introduced and utilized here [64][65][66][67][68].…”

Section: Performance Metricsmentioning

confidence: 99%

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Shi

Zhou

et al. 2020

Applied Sciences

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Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.

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“…The PSO algorithm is inspired by the behavior of social organisms in groups, such as bird and fish schooling or ant colonies. It has been applied to numerous areas and has been shown to be capable of solving multidimensional optimization problems effectively (see [ 18 , 19 , 20 , 21 ] and the references therein). Another challenge encountered in the shape fitting process is to compute the Euclidean distance (also called L2 distance) between an observation and a given shape equation in

space.…”

Section: Introductionmentioning

confidence: 99%

A Shape Approximation for Medical Imaging Data

Huang

Wen

Chu

et al. 2020

Sensors

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This study proposes a shape approximation approach to portray the regions of interest (ROI) from medical imaging data. An effective algorithm to achieve an optimal approximation is proposed based on the framework of Particle Swarm Optimization. The convergence of the proposed algorithm is derived under mild assumptions on the selected family of shape equations. The issue of detecting Parkinson’s disease (PD) based on the Tc-99m TRODAT-1 brain SPECT/CT images of 634 subjects, with 305 female and an average age of 68.3 years old from Kaohsiung Chang Gung Memorial Hospital, Taiwan, is employed to demonstrate the proposed procedure by fitting optimal ellipse and cashew-shaped equations in the 2D and 3D spaces, respectively. According to the visual interpretation of 3 experienced board-certified nuclear medicine physicians, 256 subjects are determined to be abnormal, 77 subjects are potentially abnormal, 174 are normal, and 127 are nearly normal. The coefficients of the ellipse and cashew-shaped equations, together with some well-known features of PD existing in the literature, are employed to learn PD classifiers under various machine learning approaches. A repeated hold-out with 100 rounds of 5-fold cross-validation and stratified sampling scheme is adopted to investigate the classification performances of different machine learning methods and different sets of features. The empirical results reveal that our method obtains 0.88 ±0.04 classification accuracy, 0.87 ±0.06 sensitivity, and 0.88 ±0.08 specificity for test data when including the coefficients of the ellipse and cashew-shaped equations. Our findings indicate that more constructive and useful features can be extracted from proper mathematical representations of the 2D and 3D shapes for a specific ROI in medical imaging data, which shows their potential for improving the accuracy of automated PD identification.

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Prediction of blast-produced ground vibration using particle swarm optimization

Cited by 124 publications

References 40 publications

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

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

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

A Shape Approximation for Medical Imaging Data

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