Prediction of Backbreak in the Blasting Operations using Artificial Neural Network (ANN) Model and Statistical Models (Case study: Gol-e-Gohar Iron Ore Mine No. 1) Abbas Khajouei Sirjani, Farhang Sereshki, Mohammad Ataei, Mohammad Amiri Hosseini Archives of Mining Sciences, 2022 Prediction of BackBreak in the Blasting oPerations using artificial neural network (ann) Model and statistical Models (case study: gol-e-gohar iron ore Mine no. 1)backbreak is an undesirable phenomenon in blasting operations, which can bedefined as the undesirable destruction of rock behind the last row of explosive holes.to prevent and reduce its adverse effects, it is necessary to accurately predict backbreak in the blasting process.For this purpose, the data obtained from 66 blasting operations in gol-e-gohar iron ore mine no. 1 considering blast pattern design Parameters and geologic were collected.the Pearson correlation results showed that the parameters of the hole height, burden, spacing, specific powder, number of holes, and the uniaxial compressive strength had a significant effect on the backbreak.in this study, a multilayer perceptron artificial neural network with the 6-12-1 architecture and six multiple linear and nonlinear statistical models were used to predict the backbreakin the blasting operations.the results of this study demonstrated that the prediction rate of backbreak using the artificial neural network model with R 2 = 0.798 and the rates of MAd, MSE, rMSE and, MAPE were0.79,0.93, 0.97 and, 11.63, respectively, showed fewer minor error compared to statistical models.based on the sensitivity analysis results, the most important parameters affecting the backbreak, including the hole height, distance between the holes in the same row, the row spacing of the holes, had the most significant effect on the backbreak, and the uniaxial compressive strength showed the lowest impact on it.
RECENT SCHOLAR PUBLICATIONS
Field-validated numerical investigation of rock fragmentation in conventional and air-deck blasting using LS-DYNA A case study from Gol-e-Gohar iron ore mine A Khajouei Sirjani, F Sereshki, M Ataei, M Amiri Hossaini Journal of Mining and Environment , 2026 2026
Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations AK Sirjani, F Sereshki, M Ataei, M Khandelwal, HM Anayi, SMMM Nasab, ... Results in Earth Sciences 3, 100109 , 2025 2025
Prediction and optimization of ground vibration caused by blasts using a combination of statistical models and FROG algorithm (Case study: Gol-e-Gohar Iron Ore Mine No. 1) A Khajouei Sirjani, F Sereshki, M Ataei, M Amiri Hossaini Journal of Mining and Environment 16 (4), 1403-1416 , 2025 2025 Citations: 1
The optimization of statistical models for predicting blast-induced backbreak in mining using the Firefly Algorithm A case study A Khajouei Sirjani, R Heydari, R Rafiee, M Amiri Hosseini Journal of Mining and Environment , 2025 2025
Prediction of Backbreak in the Blasting Operations using Artificial Neural Network (ANN) Model and Statistical Models (Case study: Gol-e-Gohar Iron Ore Mine No. 1) MAH Abbas Khajouei Sirjani , Farhang Sereshki , Mohammad Ataei Archives of Mining Sciences 67 (No 1), 107-121 , 2022 2022 Citations: 19
MOST CITED SCHOLAR PUBLICATIONS
Prediction of Backbreak in the Blasting Operations using Artificial Neural Network (ANN) Model and Statistical Models (Case study: Gol-e-Gohar Iron Ore Mine No. 1) MAH Abbas Khajouei Sirjani , Farhang Sereshki , Mohammad Ataei Archives of Mining Sciences 67 (No 1), 107-121 , 2022 2022 Citations: 19
Prediction and optimization of ground vibration caused by blasts using a combination of statistical models and FROG algorithm (Case study: Gol-e-Gohar Iron Ore Mine No. 1) A Khajouei Sirjani, F Sereshki, M Ataei, M Amiri Hossaini Journal of Mining and Environment 16 (4), 1403-1416 , 2025 2025 Citations: 1
Field-validated numerical investigation of rock fragmentation in conventional and air-deck blasting using LS-DYNA A case study from Gol-e-Gohar iron ore mine A Khajouei Sirjani, F Sereshki, M Ataei, M Amiri Hossaini Journal of Mining and Environment , 2026 2026
Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations AK Sirjani, F Sereshki, M Ataei, M Khandelwal, HM Anayi, SMMM Nasab, ... Results in Earth Sciences 3, 100109 , 2025 2025
The optimization of statistical models for predicting blast-induced backbreak in mining using the Firefly Algorithm A case study A Khajouei Sirjani, R Heydari, R Rafiee, M Amiri Hosseini Journal of Mining and Environment , 2025 2025
