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

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  • Xianan Wang

    (School of Civil and Architectural Engineering, Anyang Institute of Technology, Anyang 455000, China)

  • Shahab Hosseini

    (Faculty of Engineering, Tarbiat Modares University, Tehran 14115-175, Iran)

  • Danial Jahed Armaghani

    (Centre of Tropical Geoengineering (GEOTROPIK), Institute of Smart Infrastructure and Innovative Engineering (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Edy Tonnizam Mohamad

    (Centre of Tropical Geoengineering (GEOTROPIK), Institute of Smart Infrastructure and Innovative Engineering (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

Abstract

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 (R 2 ), root mean square error (RMSE) and value accounted for (VAF). The results of the calculation of evaluation indicators revealed that R 2 , 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.

Suggested Citation

  • Xianan Wang & Shahab Hosseini & Danial Jahed Armaghani & Edy Tonnizam Mohamad, 2023. "Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting," Mathematics, MDPI, vol. 11(10), pages 1-22, May.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2358-:d:1150273
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    References listed on IDEAS

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    1. Rana Muhammad Adnan Ikram & Leonardo Goliatt & Ozgur Kisi & Slavisa Trajkovic & Shamsuddin Shahid, 2022. "Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction," Mathematics, MDPI, vol. 10(16), pages 1-30, August.
    2. Hosseini, Shahab & Mousavi, Amin & Monjezi, Masoud & Khandelwal, Manoj, 2022. "Mine-to-crusher policy: Planning of mine blasting patterns for environmentally friendly and optimum fragmentation using Monte Carlo simulation-based multi-objective grey wolf optimization approach," Resources Policy, Elsevier, vol. 79(C).
    3. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2021. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Applied Mathematics and Computation, Elsevier, vol. 389(C).
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    1. Zhao, Jue & Hosseini, Shahab & Chen, Qinyang & Jahed Armaghani, Danial, 2023. "Super learner ensemble model: A novel approach for predicting monthly copper price in future," Resources Policy, Elsevier, vol. 85(PB).

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