Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model
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ORIGINAL ARTICLE
Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model Qiancheng Fang1 • Hoang Nguyen2
•
Xuan-Nam Bui3,4 • Trung Nguyen-Thoi5,6 • Jian Zhou7
Received: 22 November 2019 / Accepted: 11 July 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Rock size distribution Rock fragmentation FFA-BGAM Blasting Optimization algorithm Hybrid technique
& Hoang Nguyen [email protected]; [email protected] Xuan-Nam Bui [email protected] 1
Institute of Architecture Engineering, Huanghuai University, Zhumadian 463000, Henan, China
2
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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
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
5
Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
6
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
7
School of Resources and Safety Engineering, Central South University, Changsha 410083, Hunan, China
123
Neural Computing and Applications
1 Introduction A primary rock fragmentation method in open-pit mines, construction, and civil engineering is blasting. Through explosive energy, rock m
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