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Novel Soft ComputingModel for Predicting blast-induced ground vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

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Computer Modeling in Engineering & Sciences 2023年第3期134卷 2227-2246页

作者： Quang-Hieu Tran Hoang Nguyen Xuan-Nam Bui Department of Surface Mining Mining FacultyHanoi University of Mining and GeologyHanoi100000Vietnam Innovations for Sustainable and Responsible Mining(ISRM)Research Group Hanoi University of Mining and GeologyHanoi100000Vietnam

This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning ***,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial *** bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and *** is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as *** addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive *** this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also *** were then compiled as the dataset to develop the PPV predictive *** results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR *** those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.

关键词： Mine blasting blast-induced ground vibration environmentally friendly blasting peak particle velocity bagging extra trees

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An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine,Iran

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Journal of Rock Mechanics and Geotechnical Engineering 2014年第1期6卷 67-76页

作者： Mahdi Saadat Manoj Khandelwal M.Monjezi Department of Mining Engineering Shahid Bahonar University of Kerman Department of Mining Engineering College of Technology&EngineeringMaharana Pratap University of Agriculture&Technology Faculty of Engineering Tarbiat Modares University

blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human *** this paper,an attempt has been made to present an application of artificial neural network(ANN)to predict the blast-induced ground vibration of the Gol-E-Gohar(GEG)iron mine,Iran.A four-layer feed-forward back propagation multi-layer perceptron(MLP)was used and trained with Levenberg–Marquardt *** construct ANN models,the maximum charge per delay,distance from blasting face to monitoring point,stemming and hole depth were taken as inputs,whereas peak particle velocity(PPV)was considered as an output parameter.A database consisting of69data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN *** of determination(R2)and mean square error(MSE)were chosen as the indicators of the performance of the networks.A network with architecture4-11-5-1and R2of0.957and MSE of0.000722was found to be *** demonstrate the supremacy of ANN approach,the same69data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression(MLR)*** results revealed that the proposed ANN approach performs better than empirical and MLR models.

关键词： blast-induced ground vibration Empirical predictors Artificial neural network(ANN) Multiple linear regression

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