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Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model

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Abstract

This paper proposes a new soft computing model (artificial intelligence model) for modeling rock fragmentation (i.e., the size distribution of rock (SDR)) with high accuracy, based on a boosted generalized additive model (BGAM) and a firefly algorithm (FFA), called FFA-BGAM. Accordingly, the FFA was used as a robust optimization algorithm/meta-heuristic algorithm to optimize the BGAM model. A split-desktop environment was used to analyze and calculate the size of rock from 136 images, which were captured from 136 blasts. To this end, blast designs were collected and extracted as the input parameters. Subsequently, the proposed FFA-BGAM model was evaluated and compared through previous well-developed soft computing models, such as FFA-ANN (artificial neural network), FFA-ANFIS (adaptive neuro-fuzzy inference system), support vector machine (SVM), Gaussian process regression (GPR), and k-nearest neighbors (KNN) based on three performance indicators (MAE, RMSE, and R2). The results indicated that the new intelligent technique (i.e., FFA-BGAM) provided the highest accuracy in predicting the SDR with an MAE of 0.920, RMSE of 1.213, and R2 of 0.980. In contrast, the remaining models (i.e., FFA-ANN, FFA-ANFIS, SVM, GPR, and KNN) yielded lower accuracies in predicting the SDR, i.e., MAEs of 1.248, 1.661, 1.096, 1.573, 1.237; RMSEs of 1.598, 2.068, 1.402, 2.137, 1.717; and R2 of 0.967, 0.968, 0.972, 0.940, 0.963, respectively.

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Acknowledgements

This paper was supported by the Center for Mining, Electro-Mechanical research of Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam; the research team of Innovations for Sustainable and Responsible Mining (ISRM) of HUMG, and partially supported by the National Natural Science Foundation Project of China (Grant no. 41807259) and the Innovation-Driven Project of Central South University (2020CX040).

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Authors and Affiliations

  1. Institute of Architecture Engineering, Huanghuai University, Zhumadian, 463000, Henan, China

    Qiancheng Fang

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

    Hoang Nguyen

  3. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Xuan-Nam Bui

  4. Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Xuan-Nam Bui

  5. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Trung Nguyen-Thoi

  6. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Trung Nguyen-Thoi

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

    Jian Zhou

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Correspondence to Hoang Nguyen.

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Fang, Q., Nguyen, H., Bui, XN. et al. Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model. Neural Comput & Applic 33, 3503–3519 (2021). https://doi.org/10.1007/s00521-020-05197-8

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