Assessment of the ground vibration during blasting in mining projects using different computational approaches

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Oct. 30, 2023

The investigation compares the conventional, advanced machine, deep, and hybrid learning models to introduce an optimum computational model assess ground vibrations during blasting in mining projects. long short-term memory (LSTM), artificial neural network (ANN), least square support vector machine (LSSVM), ensemble tree (ET), decision (DT), Gaussian process regression (GPR), (SVM), multilinear (MLR) are employed using 162 data points. For first time, blackhole-optimized LSTM has been used predict blasting. Fifteen performance metrics have implemented measure prediction capabilities of models. study concludes that blackhole optimized-LSTM PPV11 is highly capable predicting vibration. Model assessed with RMSE = 0.0181 mm/s, MAE 0.0067 R 0.9951, a20 96.88, IOA 0.9719, IOS 0.0356 testing. Furthermore, this reveals accuracy less affected by multicollinearity because optimization algorithm. external cross-validation literature validation confirm PPV11. ANOVA Z tests reject null hypothesis for actual vibration, Anderson-Darling test rejects predicted This also GPR LSSVM overfit moderate problematic assessing vibration

Language: Английский

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State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(1)

Published: Jan. 1, 2024

Language: Английский

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Prediction and optimization of adverse responses for a highway tunnel after blasting excavation using a novel hybrid multi-objective intelligent model

Transportation Geotechnics, Journal Year: 2024, Volume and Issue: 45, P. 101228 - 101228

Published: March 1, 2024

Language: Английский

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Toward Precise Long-Term Rockburst Forecasting: A Fusion of SVM and Cutting-Edge Meta-heuristic Algorithms

Natural Resources Research, Journal Year: 2024, Volume and Issue: 33(5), P. 2037 - 2062

Published: June 19, 2024

Language: Английский

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Advanced Machine Learning Methods for Prediction of Blast-Induced Flyrock Using Hybrid SVR Methods

Computer Modeling in Engineering & Sciences, Journal Year: 2024, Volume and Issue: 140(2), P. 1595 - 1617

Published: Jan. 1, 2024

Blasting in surface mines aims to fragment rock masses a proper size.However, flyrock is an undesirable effect of blasting that can result human injuries.In this study, support vector regression (SVR) combined with four algorithms: gravitational search algorithm (GSA), biogeography-based optimization (BBO), ant colony (ACO), and whale (WOA) for predicting two Iran.Additionally, three other methods, including artificial neural network (ANN), kernel extreme learning machine (KELM), general (GRNN), are employed, their performances compared those hybrid SVR models.After modeling, the measured predicted values validated some performance indices, such as root mean squared error (RMSE).The results revealed SVR-WOA model has most optimal accuracy, RMSE 7.218, while RMSEs KELM, GRNN, SVR-GSA, ANN, SVR-BBO, SVR-ACO models 10.668, 10.867, 15.305, 15.661, 16.239, 18.228, respectively.Therefore, combining WOA be valuable tool accurately distance mines.

Language: Английский

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Application of supervised random forest paradigms based on optimization and post-hoc explanation in underground stope stability prediction

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 154, P. 111388 - 111388

Published: Feb. 12, 2024

Language: Английский

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Migration time prediction and assessment of toxic fumes under forced ventilation in underground mines

Underground Space, Journal Year: 2024, Volume and Issue: 18, P. 273 - 294

Published: April 26, 2024

This study aims to predict the migration time of toxic fumes induced by excavation blasting in underground mines. To reduce numerical simulation and optimize ventilation design, several back propagation neural network (BPNN) models optimized honey badger algorithm (HBA) with four chaos mapping (CM) functions (i.e., Chebyshev (Che) map, Circle (Cir) Logistic (Log) Piecewise (Pie) map) are developed time. 125 simulations computational fluid dynamics (CFD) method used train test models. The determination coefficient (R2), variance accounted for (VAF), Willmott's index (WI), root mean square error (RMSE), absolute percentage (MAPE), sum squares (SSE) utilized evaluate model performance. evaluation results indicate that CirHBA-BPNN has achieved most satisfactory performance reaching highest values R2 (0.9945), WI (0.9986), VAF (99.4811%), lowest RMSE (15.7600), MAPE (0.0343) SSE (6209.4), respectively. wind velocity roadway (Wv) is important feature predicting fumes. Furthermore, intrinsic response characteristic optimal implemented enhance interpretability provide reference relationship between features design.

Language: Английский

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Prediction of time-dependent bearing capacity of concrete pile in cohesive soil using optimized relevance vector machine and long short-term memory models

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 30, 2024

Language: Английский

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A stacked multiple kernel support vector machine for blast induced flyrock prediction

Geohazard Mechanics, Journal Year: 2024, Volume and Issue: 2(1), P. 37 - 48

Published: Jan. 23, 2024

As a widely used rock excavation method in Civil and Mining construction works, the blasting operations its induced side effects are always investigated by existing studies. The occurrence of flyrock is regarded as one most important issues operations, since accurate prediction which crucial for delineating safety zone. For this purpose, study developed model based on 234 sets data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stacked MK-SVM) was proposed prediction. structure can effectively improve performance addressing importance level different features. comparison 6 other learning models were developed, including SVM, MK-SVM, Lagragian twin SVM (LTSVM), artificial neural network (ANN), random forest (RF) M5 Tree. This implemented 5-fold cross validation process hyperparameters tuning purpose. According to evaluation results, MK-SVM achieved best overall performance, with RMSE 1.73 1.74, MAE 0.58 1.08, VAF 98.95 99.25 training testing phase respectively.

Language: Английский

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Predicting energy absorption characteristic of rubber concrete materials

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 465, P. 140248 - 140248

Published: Feb. 1, 2025

Language: Английский

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