Determination of blast-induced flyrock using a drone technology: a bibliometric overview with practical soft computing implementation

Lawal, Abiodun Ismail; Ojo, Oluwabankole Joseph; Kim, Min-Ju; Kwon, Soonjo

doi:10.1007/s12517-022-10770-7

Cited by 4 publications

(2 citation statements)

References 81 publications

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“…Year Inputs AI Algorithm [1] HD Empirical [26] HD, SC Empirical [2] ST/B Empirical [27] B, ST, HD Empirical [15] HD, Fs Empirical [28] HD, ST, BS, SD, PF, Qmax, N, RD ANN [7] B, S, HD, ST, SD, PF, Qmax, RD FIS, SM [8] B, S, HD, ST, PF, Qmax, BI, RMR ANN [9] d, B, HD, ST, BS, SD, PF, Qmax, BI ANN [29] B, S, HD, ST, SD, PF, Qmax, RMR ANN-GA [10] PF, HD, SD, S, d, B, ST ANN, SVM [30] B, S, ST, HD, HD, SC, Q Empirical [31] HD, S, B, d, Qmax ANN [32] HD, S, B, ST, PF, SD SVM [13] B, S, CPM, Q, σ c , RQD MVRA [14] PF, S, HD, ST, B, Qmax FIS, ANN [33] d, B, S, HD, Q, CPM, σ c , RQD ANN, ANFIS [34] BDF, EDF, RMR Empirical [35] B, S, CPM, PF, σ c , RQD MVRA, BPNN [36] B/S, H/B, SD, PF, Qmax RD LS-SVM, SVR [37] B/d, S/B, ST/B, H/B, PF, Xb MDA [38] B, S, ST, PF RD ELM [39] B, S/B, ST/B, H/B, d, B/d, PF, Qmax, VoD RMR, BI FRES [6] B, S, ST, PF, Q Z-FCM-ANN [3] N, HD, B, S, ST, BRH, PF, Q ANN [40] ST, Q, PF ANN [41] d, HD, S, PF, B/S, ST, Qmax ANFIS, HGSO-ANFIS [42] HD, S, B, ST, PF DT, XGBoost, AdaBoost…”

Section: Referencementioning

confidence: 99%

Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

et al. 2023

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One of the most undesirable consequences induced by blasting in open-pit mines and civil activities is flyrock. Furthermore, the production of oversize boulders creates many problems for the continuation of the work and usually imposes additional costs on the project. In this way, the breakage of oversize boulders is associated with throwing small fragments particles at high speed, which can lead to serious risks to human resources and infrastructures. Hence, the accurate prediction of flyrock induced by boulder blasting is crucial to avoid possible consequences and its’ environmental side effects. This study attempts to develop an optimized artificial neural network (ANN) by particle swarm optimization (PSO) and jellyfish search algorithm (JSA) to construct the hybrid models for anticipating flyrock distance resulting in boulder blasting in a quarry mine. The PSO and JSA algorithms were used to determine the optimum values of neurons’ weight and biases connected to neurons. In this regard, a database involving 65 monitored boulders blasting for recording flyrock distance was collected that comprises six influential parameters on flyrock distance, i.e., hole depth, burden, hole angle, charge weight, stemming, and powder factor and one target parameter, i.e., flyrock distance. The ten various models of ANN, PSO–ANN, and JSA–ANN were established for estimating flyrock distance, and their results were investigated by applying three evaluation indices of coefficient of determination (R2), root mean square error (RMSE) and value accounted for (VAF). The results of the calculation of evaluation indicators revealed that R2, values of (0.957, 0.972 and 0.995) and (0.945, 0.954 and 0.989) were determined to train and test of proposed predictive models, respectively. The yielded results denoted that although ANN model is capable of anticipating flyrock distance, the hybrid PSO–ANN and JSA–ANN models can anticipate flyrock distance with more accuracy. Furthermore, the performance and accuracy level of the JSA–ANN predictive model can estimate better compared to ANN and PSO–ANN models. Therefore, the JSA–ANN model is identified as the superior predictive model in estimating flyrock distance induced from boulder blasting. In the final, a sensitivity analysis was conducted to determine the most influential parameters in flyrock distance, and the results showed that charge weight, powder factor, and hole angle have a high impact on flyrock changes.

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Section: Referencementioning

confidence: 99%

Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

et al. 2023

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“…Rapid industrialization and urbanization have increased the demand for new and improved infrastructural amenities that have continued to rise in recent years, owing to the continuous and astronomical surge in human population, which has overstretched the existing constructed infrastructure worldwide. Rock excavation by blasting has been widely and successfully used in recent years in civil construction works, including urban land and redevelopment construction projects, dam and hydropower projects, tunneling and underground spaces, and mineral exploitation, because of its affordability, applicability to any geological terrain and lithologies, and the production of reliable results in comparison with other methods [1][2][3][4][5]. However, some percentage of the confined explosive energy that is meant for rock fragmentation during blasting escapes into the surrounding space, causing various unwanted environmental events, including air blast or air overpressure (AOp), blast-induced ground vibration (BIGV), flyrock, overbreak, and noxious gases (Figure 1) [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Development of an ANN-Based Closed-Form Equation for the Prediction of Airblast Overpressure Induced by Construction Rock Excavation Blasting in Urban Areas

Ogunsola

Shin

Kim³

et al. 2022

Applied Sciences

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Blasting has been proven to be the most cost-effective method for rock excavation known to man. The cost-effectiveness advantage of blasting is overshadowed by its unpleasant environmental problems, particularly at construction sites close to human settlements and public utilities. Therefore, efforts are required to develop closed-form equations that can accurately predict environmental problems associated with blasting. This study proposes an ANN-based closed-form explicit equation for forecasting airblast overpressure (AOp) at multiple construction sites in South Korea. Nine important factors that affect AOp generation were used to develop the model. First, a stand-alone ANN was initiated, and the hyperparameters of the optimum ANN structure were tuned using two novel and robust metaheuristic algorithms: the slime mould algorithm (SMA) and multi-verse optimization (MVO). To appraise the predictive accuracy of the developed soft computing models, multilinear regression (MLR) and a generalized empirical predictor were developed for comparison. The analysis showed that the SMA-ANN and MVO-ANN models predicted AOp with the highest accuracy compared with the other models. The two hybrid ANN-based models were transformed into closed-form and explicit equations to aid in the easy forecasting of AOp when planning a blasting round at construction sites. The developed model equations were validated for practical engineering applications and a comprehensive relative importance analysis of the AOp input parameters was performed. The relevance importance analysis shows that the rock mass rating (RMR), charge per delay (Q), and monitoring distance (DIS) have the highest impacts on AOp.

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Soft Computing Applications for Optimum Rock Fragmentation: An Advanced Overview

et al. 2023

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Determination of blast-induced flyrock using a drone technology: a bibliometric overview with practical soft computing implementation

Cited by 4 publications

References 81 publications

Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

Development of an ANN-Based Closed-Form Equation for the Prediction of Airblast Overpressure Induced by Construction Rock Excavation Blasting in Urban Areas

Soft Computing Applications for Optimum Rock Fragmentation: An Advanced Overview

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