Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm

Lawal, Abiodun Ismail; Olajuyi, Seun Isaiah; Kwon, Soonjo; Aladejare, Adeyemi Emman; Edo, T. M.

doi:10.1007/s11600-021-00607-4

Cited by 13 publications

(3 citation statements)

References 23 publications

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“…These methods can effectively handle the complexities that arise from the nonlinear relationships between the variables that influence blasting. Methods that have been explored to improve the study of PPV include: ➤ Artificial neural networks (ANNs) (Amnieh, Mozdianfard, and Siamaki, 2010;Amnieh, Siamaki, and Soltani, 2012;Azimi, Khoshrou, and Osanloo, 2019;Das, Sinha, and Ganguly, 2019;Jiang et al, 2019;Kamali and Ataei, 2010;Kosti et al, 2013;Ragam and Nimaje, 2019;Sayadi et al, 2013) ➤ Other machine-learning studies (Lawal, Olajuyi, and Kwon, 2021;Longjun et al, 2011) ➤ Numerical methods (Ducarne et al, 2018;Kumar et al, 2020;Nguyen and Gatmiri, 2007) ➤ Multivariate analysis (Hudaverdi, 2012) ➤ Empirical analysis (Hu and Qu, 2018) ➤ Bayesian approach (Aladejare, Lawal, and Onifade, 2022).…”

Section: Introductionmentioning

confidence: 99%

Prediction of ground vibrations induced by bench blasting using the random forest algorithm

Dzimunya,

Besa,

Nyirenda

2023

J. S. Afr. Inst. Min. Metall.

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The accurate estimation of peak particle velocity (PPV) is crucial during the design of bench blasting operations in open pit mines, since the vibrations caused by blasting can significantly affect the integrity of nearby buildings and other structures. Conventional models used to predict blast-induced vibrations are not capable of capturing nonlinear relationships between the different blasting-related parameters. Soft computing techniques, i.e., techniques that are founded on the principles of artificial intelligence, effectively model these complexities. In this paper, we use the random forest (RF) algorithm to develop a model to predict blast-induced ground vibrations from bench blasting using 48 data records. The model was trained and tested using WEKA data-mining software. To build this model, a feature selection process using several combinations of Attribute Evaluators and Search Methods under the WEKA Select Attributes tab was performed. The correlation coefficient of the actual data and RF model-predicted data was 0.95, and the weighted average of the relative absolute error (RAE) was 10.9%. The RF model performance was also compared to the equivalent-path-based (EPB) equation on the testing data-set, and it was seen that the RF model can effectively be used to predict PPV. The study also demonstrates that the EPB equation is a suitable empirical method for predicting PPV.

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Section: Introductionmentioning

confidence: 99%

Prediction of ground vibrations induced by bench blasting using the random forest algorithm

Dzimunya,

Besa,

Nyirenda

2023

J. S. Afr. Inst. Min. Metall.

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“…To predict the hazard level of blasting vibration that may be generated by blasting operations, so that effective vibration reduction measures can be taken to cope with it and thus control blasting vibration hazards. Lawal et al 11 used Gaussian process regression to predict the ground vibration rate caused by blasting, and used the Gray Wolf algorithm to optimize the neural network to achieve the goal of optimizing blasting parameters, thus minimizing the ground vibration caused by blasting operations in quarries.…”

Section: Introductionmentioning

confidence: 99%

Blasting vibration hazard classification and prediction research

Zhang

Wang

Yang

et al. 2023

Advances in Mechanical Engineering

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This paper explores the hazard classification method of blasting vibration on nearby buildings. The ratio of the self-vibration frequency f0 of the protected buildings to the main vibration frequency f of blasting vibration and the ratio of the actual measured peak blasting vibration velocity to the permissible vibration velocity of blasting safety regulations are used as two indicators of the blasting vibration hazard classification method, the blasting vibration hazard index calculation formula was proposed, and the blasting vibration hazard was classified into four levels according to the blasting vibration hazard index. An open pit mine blasting project was used for monitoring, the collected data were processed by random forest method. After processing, bursting heart distance, the total explosive quantity required for one blast, the hole distance meter delay, and the row distance meter delay was selected as the input parameters; the optimal kernel function parameter gamma and c were applied to the SVR (Support Vector Regression) model by changing the search step to expand the parameter search, and improved GS-SVR (Grid Search-Support Vector Regression) model was constructed, through which peak blasting vibration velocity and vibration frequency values were predicted. The results show: improved GS-SVR is effective in predicting peak blasting vibration velocity, with the lowest relative error of 0.15% and the average error of 7.96%; the minimum relative error for the prediction of vibration frequency is 0.03% and the average error is 2.54%. The measured data in the literature of related scholars verified that the blasting vibration hazard classification method is scientific and feasible. It can provide reference and reference for blasting vibration hazard classification and prediction of similar blasting projects.

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“…During rock blasting construction, explosives release a large amount of energy to destroy rocks, and part of the energy propagates outward in the form of seismic waves, thus causing surface vibration [9,10]. At present, a lot of research work has been carried out on blasting vibration.…”

Section: Introductionmentioning

confidence: 99%

Discussion on Blasting Vibration Velocity of Deep Rock Mass Considering Thickness of Overlying Soil Layer

Ruan¹,

Wang²,

Zhao³

et al. 2023

Period. Polytech. Civil Eng.

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The thickness of the overlying soil layer has a certain influence on the blasting vibration response of deep rock mass. The vibration wave velocity of the overlying soil layer during the construction of deep blasting is measured in this paper. Based on the measured data, parameters k and α of the Sadowski equation are used to characterize the influence of the comprehensive geological conditions of the site on the vibration wave propagation. The model of blasting vibration velocity of deep rock mass is established according to the existing blasting theory, and the calculation accuracy of the model is verified according to the field blasting parameters. The new model is used to simulate different overlying soil thicknesses, and the safe allowable distance under different soil thicknesses is calculated. The calculated results show that with the increase of the thickness of the overlying soil layer, the blasting vibration velocity decreases and the attenuation velocity decreases gradually. The research results reveal the reduction effect of overlying soil thickness on blasting vibration to some extent. In the area with overlying soil layer, the safe allowable distance of blasting vibration safety can be appropriately reduced to increase the land utilization rate, which has important reference value for the blasting design and safety prediction of deep rock mass.

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Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm

Cited by 13 publications

References 23 publications

Prediction of ground vibrations induced by bench blasting using the random forest algorithm

Prediction of ground vibrations induced by bench blasting using the random forest algorithm

Blasting vibration hazard classification and prediction research

Discussion on Blasting Vibration Velocity of Deep Rock Mass Considering Thickness of Overlying Soil Layer

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