Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Different Nature-Inspired Optimization Algorithms and Deep Neural Network

Nguyen, Hoang; Bui, Xuan‐Nam; Tran, Quang-Hieu; Nguyen, Dinh-An; Hoa, Le Thi Thu; Le, Qui-Thao; Giang, Le Thi

doi:10.1007/s11053-021-09896-4

Cited by 20 publications

(7 citation statements)

References 77 publications

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“…On the other hand, the use of deep learning, which is gaining much popularity due to its supremacy in terms of accuracy in recent years, to predict blast-induced PPV is relatively new. For example, Nguyen et al (2021) have developed a deep neural network (DNN) and used whale, Harris hawks, and particle swarm algorithms to optimize it. Results showed that the optimized DNN predicts blast-induced PPV with outstanding accuracy.…”

Section: Sc Methodsmentioning

confidence: 99%

Blast-Induced Ground Vibration Prediction and Uncertainty Quantification in Granite Quarries Using Deep Ensembles Model

Erten

Keser

Yavuz

2022

Preprint

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Ground vibration is one of the most dangerous environmental problems associated with blasting operations in mining. Therefore, accurate prediction and controlling the blast-induced ground vibration are imperative for environmental protection and sustainable development. The empirical approaches give inaccurate results as obvious in the literature, hence, numerous researchers have started to use the fast-growing soft computing approaches with satisfying prediction performance. However, not only achieving the high-prediction performance but also detecting prediction uncertainty is very important for especially blasting operations which is a safety-critical system. This study aims to propose the deep ensembles model to predict the blast-induced ground vibration and quantify the prediction uncertainty, which is usually not addressed. The deep ensembles model includes training an ensemble of deep neural networks (DNN) on the same data, by initializing each network randomly, to yield multiple predictions on both the predicted value and the uncertainty value. In this study, 200 published data from ten granite quarry sites in Ibadan and Abeokuta areas, Nigeria were used. The input parameters were the distance from the blasting face and the weight of the charge per delay, while the targeted output was the peak particle velocity (PPV). For comparison, a single DNN model and an empirical equation (USBM equation) were applied. According to the coefficient of determination\({\text{R}}^{2}\), root mean squared error (RMSE), negative log-likelihood (NLL), mean prediction interval width (MPIW), and prediction interval coverage probability (PICP), the deep ensembles model gave both the most accurate predictions and the most reliable predictive uncertainty quantification.

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Section: Sc Methodsmentioning

confidence: 99%

Blast-Induced Ground Vibration Prediction and Uncertainty Quantification in Granite Quarries Using Deep Ensembles Model

Erten

Keser

Yavuz

2022

Preprint

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“…Aiming at the prediction of the surface deformation of the mine slope, many experts and scholars have porposed a variety of different prediction methods. Currently, open-pit slope deformation prediction models mainly include the statistical model, the deterministic model, and the artificial intelligence model [3]. The artificial intelligence model is suitable for the construction of complex nonlinear models, which significantly improves the accuracy of slope prediction [4].…”

Section: Introductionmentioning

confidence: 99%

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

Du,

Song,

et al. 2024

ELEKTRON ELEKTROTECH

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At present, due to the complex and changeable geological conditions, the precise deformation prediction technology of high and steep slope could not achieve an accurate prediction. In particular, the single forecasting model has some problems such as poor stability, low precision, and data fluctuation. In practice, excavating the complex nonlinear relationship between open-pit slope surface deformation monitoring data and various influencing factors and improving the accuracy of the deformation prediction of high and steep slopes is the key to safe open-pit mine production. It proposed to introduce the position factor and the velocity factor into a twin support vector machine (TWSVM). The adaptive subgroup optimisation (APSO) algorithm is selected for parameter optimisation. Through the comparative analysis of TWSVM, genetic algorithm-TWSVM (GA-TWSVM), and the proposed APSO⁃TWSVM, the experimental data show that the mean absolute error (MAE) values of the three models are 13.29 %，8.17 %, and 1.27 %, the RMSE - 47.83 %，6.52 %, and 3.02 %, respectively; the prediction time for APSO⁃TWSVM is improved by 62.5 % compared to GA-TWSVM.

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“…To gain better results, artificial intelligence (AI) methods/models have been proposed to predict PPV with many advantages, such as high accuracy, rock properties are considered, different blasting parameters are investigated and applied, low-cost, and time-saving. A variety of AI models have been proposed for the aims of PPV prediction and control in open-pit mines, such as artificial neural networks (ANN) models [17][18][19][20], machine learning-based models (e.g., support vector machine, CART, multivariate statistical analysis, multivariate adaptive regression splines, to name a few) [21][22][23][24][25], metaheuristic algorithm-based ANN models [1,5,[26][27][28][29][30], metaheuristic algorithm-based machine learning models [31][32][33][34][35][36][37], and clustering-based models [38][39][40][41]. Therein, the accuracies of the introduced models are in the range of 92.7%-98.6%.…”

Section: Introductionmentioning

confidence: 99%

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

Tran¹,

Nguyen²,

Bui³

2023

Computer Modeling in Engineering &Amp; Sciences

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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 algorithms. Accordingly, 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 prediction. The 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, BA-KNN, BA-SVR, and BA-DTR. It 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 well. In addition, two empirical models (i.e., USBM and Ambraseys) were also treated and compared with the bagging models to gain a comprehensive assessment. With this aim, we collected 300 blasting events with different parameters at the Sin Quyen copper mine (Vietnam), and the produced PPV values were also measured. They were then compiled as the dataset to develop the PPV predictive models. The results revealed that the bagging models provided better performance than the empirical models, except for the BA-DTR model. Of 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.

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Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Different Nature-Inspired Optimization Algorithms and Deep Neural Network

Cited by 20 publications

References 77 publications

Blast-Induced Ground Vibration Prediction and Uncertainty Quantification in Granite Quarries Using Deep Ensembles Model

Blast-Induced Ground Vibration Prediction and Uncertainty Quantification in Granite Quarries Using Deep Ensembles Model

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

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