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Optimization of Genetic Algorithm through Use of Back Propagation Neural Network in Forecasting Smooth Wall Blasting Parameters

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  • Ying Chen

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China
    China Tin Group Co., Ltd., Liuzhou 545026, China
    School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China)

  • Shirui Chen

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Zhengyu Wu

    (School of Engineering, Fujian Jiangxia University, Fuzhou 350108, China)

  • Bing Dai

    (School of Resource Environment and Safety Engineering, University of South China, Hengyang 421001, China)

  • Longhua Xv

    (School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China)

  • Guicai Wu

    (China Tin Group Co., Ltd., Liuzhou 545026, China)

Abstract

With the continuous development in drilling and blasting technology, smooth wall blasting (SWB) has been widely applied in tunnel construction to ensure the smoothness of tunnel profile, diminish overbreak and underbreak, and preserve the tunnel’s interior design shape. However, the complexity of the actual engineering environment and the deficiency of current optimization theories have posed certain challenges to the optimization of SWB parameters under arbitrary geological conditions, on the premise that certain control targets are satisfied. Against the above issue, a genetic algorithm (GA) and back propagation (BP) neural network-based computational model for SWB design parameter optimization is proposed. This computational model can comprehensively reflect the relation among geological conditions, design parameters, and results by training and testing the 285 collected sets of test data samples at different conditions. Moreover, it automatically searches optimal blasting design parameters through the control of SWB targets to acquire the optimal design parameters based on specific geological conditions of surrounding rocks and under the specified control targets. When the optimization algorithm is compared with other current optimization algorithms, it is shown that this algorithm has certain computational superiority over the existing models. When the optimized results are applied in practical engineering, it is shown that in overall consideration of the geological conditions, control targets, and other influencing factors, the proposed GA_BP-based model for SWB parameter optimization has high feasibility and reliability, and that its usage can be generalized to analogous tunneling works.

Suggested Citation

  • Ying Chen & Shirui Chen & Zhengyu Wu & Bing Dai & Longhua Xv & Guicai Wu, 2022. "Optimization of Genetic Algorithm through Use of Back Propagation Neural Network in Forecasting Smooth Wall Blasting Parameters," Mathematics, MDPI, vol. 10(8), pages 1-21, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1271-:d:791596
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    References listed on IDEAS

    as
    1. Bing Dai & Ying Chen & Guoyan Zhao & Weizhang Liang & Hao Wu, 2019. "A Numerical Study on the Crack Development Behavior of Rock-Like Material Containing Two Intersecting Flaws," Mathematics, MDPI, vol. 7(12), pages 1-16, December.
    2. Chonghao Zhu & Jianjing Zhang & Yang Liu & Donghua Ma & Mengfang Li & Bo Xiang, 2020. "Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 100(1), pages 173-204, January.
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    Cited by:

    1. Bing Dai & Guifeng Zhao & Lei Zhang & Yong Liu & Zhijun Zhang & Xinyao Luo & Ying Chen, 2022. "Energy Dissipation of Rock with Different Parallel Flaw Inclinations under Dynamic and Static Combined Loading," Mathematics, MDPI, vol. 10(21), pages 1-22, November.
    2. Shaofeng Wang & Xin Cai & Jian Zhou & Zhengyang Song & Xiaofeng Li, 2022. "Analytical, Numerical and Big-Data-Based Methods in Deep Rock Mechanics," Mathematics, MDPI, vol. 10(18), pages 1-5, September.

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