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A Novel Combination of Gradient Boosted Tree and Optimized ANN Models for Forecasting Ground Vibration Due to Quarry Blasting

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Abstract

An ensemble technique namely gradient boosted tree (GBTs) and several optimized neural network models were hybridized to predict peak particle velocity (PPV) caused by quarry blasting. The GBT was employed for choosing the most important input parameters on PPV results. Therefore, this model selected five input variables, comprising maximum charge per delay, distance, powder factor, and sub-drilling, and RQD. Once the input assortment was performed, five neural network models, including a typical artificial neural network (ANN) and ANNs with weight optimization (forward, backward, particle swarm optimization, PSO, and evolutionary), were implemented utilizing the inputs picked by the GBT. These models were assessed by several performance criteria, including the “correlation coefficient”, “root mean square error”, “variance accounted for”, “a20-index”, and a simple ranking system, as well as optimized weights. The results of hybridization showed that ANN-PSO model outperformed other models in terms of system error and accuracy. Altogether, this study's findings implied that consolidating the ensemble machine learning technique and optimized ANN models, particularly PSO could result in perfect and straightforward to understand predictions of PPV caused by quarry blasting.

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Acknowledgments

We acknowledge the financial support from the National Natural Science Foundation of China (51974043, 51774058). In addition, the first corresponding author thanks the Science and Technology Planning Project of Chongqing Education Commission (KJQN201804305) (JG-KJ-2019-006).

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Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Kang Peng

  2. Department of Transportation and Municipal Engineering, Chongqing Jianzhu College, Chongqing, 400072, China

    Jie Zeng

  3. Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, Chelyabinsk, 454080, Russian Federation

    Danial Jahed Armaghani

  4. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mahdi Hasanipanah

  5. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Qiusong Chen

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  1. Kang Peng
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  4. Mahdi Hasanipanah
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Correspondence to Jie Zeng or Mahdi Hasanipanah.

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Peng, K., Zeng, J., Armaghani, D.J. et al. A Novel Combination of Gradient Boosted Tree and Optimized ANN Models for Forecasting Ground Vibration Due to Quarry Blasting. Nat Resour Res 30, 4657–4671 (2021). https://doi.org/10.1007/s11053-021-09899-1

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