Predicting Blast-induced Ground Vibration in Quarries Using Adaptive Fuzzy Inference Neural Network and Moth–Flame Optimization

Bui, Xuan‐Nam; Nguyen, Hoang; Tran, Quang-Hieu; Nguyen, Dinh-An; Bui, Hoang‐Bac

doi:10.1007/s11053-021-09968-5

Cited by 15 publications

(4 citation statements)

References 67 publications

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“…However, the environmental impacts induced by this method are significant, such as ground vibration (GV), flyrock, air over-pressure, and air pollution [2][3][4]. Of the mentioned impacts, GV is recommended as the most dangerous problem that can threaten the structural integrity of surrounding structures, benches, and slope stability [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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Section: Introductionmentioning

confidence: 99%

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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“…[17]. The accuracy of the catchment models is highly varied due to the quality of catchment data [39]. Only some catchments are gauged to have meteorological and discharge data and other catchment characteristics on a temporal and spatial basis.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies recommended ANFIS as a highly accurate algorithm for predictions [38], [40]. Xuan-Nam et al [39] have proposed an ML model for blast-induced ground vibration predictions in quarries. They have employed several state-of-the-art algorithms, such as Moth-flame optimization-based ANFIS, XGBoost, ANN, and SVM.…”

Section: Introductionmentioning

confidence: 99%

Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

Rathnayake

Chathuranika

et al. 2023

IEEE Access

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The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFISbased model outperforms the other algorithms. The correlation coefficient of each algorithm's predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.

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Enhancing Mine Blasting Safety: Developing Intelligent Systems for Accurate Flyrock Prediction through Optimized Group Method of Data Handling Methods

Ding,

Hasanipanah,

Monjezi

et al. 2024

Nat Resour Res

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Predicting Blast-induced Ground Vibration in Quarries Using Adaptive Fuzzy Inference Neural Network and Moth–Flame Optimization

Cited by 15 publications

References 67 publications

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

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

Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka

Enhancing Mine Blasting Safety: Developing Intelligent Systems for Accurate Flyrock Prediction through Optimized Group Method of Data Handling Methods

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