Journal of Rock Mechanics and Geotechnical Engineering · 2013 · 164 citations · 19 references
EngineeringMachine LearningBlastingAnn-based ApproachVibration AnalysisDetonation PhysicsExplosive EngineeringDamage AssessmentExplosionsGeotechnical EngineeringVibration EnvironmentMine SafetySystems EngineeringMining EngineeringBlast LoadingBlasting EngineeringGround MotionEarthquake EngineeringStructural Health MonitoringMine DesignParticle VelocitySeismologyBlast-induced Ground VibrationCivil EngineeringGround VibrationGeomechanicsRock BurstBlast EngineeringGeochemistryRock FragmentationArtificial Neural Network
Blast‑induced ground vibration is an inevitable consequence of mining blasts that can damage rock, structures, and people. This study applies an artificial neural network to predict blast‑induced ground vibration at the Gol‑E‑Gohar iron mine in Iran. A four‑layer feed‑forward MLP trained with the Levenberg–Marquardt algorithm used inputs of charge per delay, distance, stemming, and hole depth from 69 mine records to predict peak particle velocity, with R² and MSE as performance indicators. The optimized 4‑11‑5‑1 network achieved R² = 0.957 and MSE = 0.000722, outperforming empirical and multiple‑linear‑regression models.
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 beings. In 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 algorithm. To 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 of 69 data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models. Coefficient of determination (R2) and mean square error (MSE) were chosen as the indicators of the performance of the networks. A network with architecture 4-11-5-1 and R2 of 0.957 and MSE of 0.000722 was found to be optimum. To demonstrate the supremacy of ANN approach, the same 69 data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression (MLR) analysis. The results revealed that the proposed ANN approach performs better than empirical and MLR models.
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