Document Type : Original Research Paper
Authors
Mining Engineering, Federal University of Technology Akure, Nigeria
Abstract
This study developed and assessed several artificial intelligence (AI) models for predicting blast-induced toe volume in small-scale dolomite mines located in the Akoko Edo Local Government Area, Edo State, Nigeria. Seven predictive models were constructed: Adaptive Boosting (AdaBoost), Random Forest (RF), Gaussian Process Regression (GPR), Support Vector Regression (SVR), a conventional Artificial Neural Network (ANN), and two Imperialist Competitive Algorithm-optimized ANNs (ICA-ANNs). The models were trained using eight input parameters including uniaxial compressive strength (UCS), spacing (S), burden (B), sub-drill (SB), drill hole length (DHL), stiffness ratio (SR), maximum instantaneous charge (MIC), and powder factor (K) with blast toe volume (TV) as the target output. Input data were collected through a combination of field measurements and laboratory analyses. Among all the models evaluated, the ICA-ANN with an 8-7-1 architecture achieved the highest predictive accuracy. It outperformed AdaBoost by 9.17%, SVR by 7.20%, GPR by 5.56%, RF by 4.75%, a standard ANN (8-5-1) by 0.78%, and a standard ANN (8-7-1) by 0.28%, based on mean squared error (MSE) and coefficient of determination (R²) metrics. Furthermore, the ICA-ANN model was applied to optimize blast design parameters. The optimal values obtained were: spacing = 1.0 m, burden = 0.8 m, sub-drill = 0.6 m, MIC = 0.72 kg, and powder factor = 0.65 kg/m³. These optimized parameters reduced the blast toe volume by 20.05%, from 209.50 m³ to 154.87 m³. The results highlight the robustness and efficiency of the ICA-ANN model for blast design optimization. By improving fragmentation quality and minimizing residual toe volume, the approach offers a practical pathway for enhancing both productivity and cost-effectiveness in small-scale mining operations.
Keywords
Main Subjects
- Kecojevic, V., & Radomsky, M. (2005). Flyrock phenomena and area security in blasting-related accidents. Safety science, 43(9), 739-750.
- Kiani, M., Hosseini, S. H., Taji, M., & Gholinejad, M. (2019). Risk assessment of blasting operations in open pit mines using fahp method. Mining Mineral Deposits.
- Taiwo, B. O., Yewuhalashet, F., Ogunyemi, O. B., Babatuyi, V. A., Okobe, E. I., & Orhu, E. A. (2023). Quarry Slope Stability Assessment Methods with Blast Induced Effect Monitoring in Akoko Edo, Nigeria. Geotechnical and Geological Engineering, 41(4), 2553-2571.
- Yu, Z., Shi, X. Z., Zhang, Z. X., Gou, Y. G., Miao, X. H., & Tang, J. Z. (2023). Using a dividing open-pit blast (DOPB) method to reduce ore loss and dilution caused by blast-induced rock movement. Acta Geotechnica, 1-17.
- Leng, Z., Fan, Y., Gao, Q., & Hu, Y. (2020). Evaluation and optimization of blasting approaches to reducing oversize boulders and toes in open-pit mine. International Journal of Mining Science and Technology, 30(3), 373-380.
- Sadeghi, F., Monjezi, M., & Jahed Armaghani, D. (2020). Evaluation and optimization of prediction of toe that arises from mine blasting operation using various soft computing techniques. Natural Resources Research, 29, 887-903.
- Kahraman, E., & Kilic, A. M. (2023). Determination of the Effective Blasting Region by Using Fragmentation Analysis: A Field Study. Iranian Journal of Science, 47(3), 791-799.
- Aubertin, J. D., Wimmer, M., & Sedghi, M. (2023). Development of site specific blasting index parameters based on single hole blast test cratering. Mining Technology, 1-14.
- Taiwo, B. O. (2023). Improvement of small-scale dolomite mine blast fragmentation efficiency using hybrid artificial intelligence and soft computing approaches—a case study. Arabian Journal of Geosciences, 16(12), 1-18.
- Odeyemi, O. Y., Taiwo, B. O., & Alaba, O. (2023). Influence of explosive maximum instantaneous charge on blasting environmental impact. Journal of Sustainable Mining, 22(4), 343.
- Zhang, Z., Qiu, X., Shi, X., Luo, Z., Chen, H., & Zong, C. (2023). Burden effects on rock fragmentation and damage, and stress wave attenuation in cut blasting of large-diameter long-hole stopes. Rock Mechanics and Rock Engineering, 1-19.
- Hamze, F., & Samareh, H. (2022). Optimization of blast parameters based on geo-mechanical properties of rock to prevent creation of toes and boulders in mine benches. Journal of Mineral Resources Engineering, 7(4), 81-102.
- Taiwo, B. O. (2022). Effect of charge load proportion and blast controllable factor design on blast fragment size distribution. Journal of Brilliant Engineering, 3(4658), 1.
- Kinyua, E. M., Jianhua, Z., Kasomo, R. M., Mauti, D., & Mwangangi, J. (2022). A review of the influence of blast fragmentation on downstream processing of metal ores. Minerals Engineering, 186, 107743.
- Taherkhani, H., & Doostmohammadi, R. (2015). Transportation costs: A tool for evaluating the effect of rock mass mechanical parameters on blasting results in open pit mining. Journal of Mining Science, 51, 730-742.
- Al-Bakri, A., & Hefni, M. (2021). A review of some nonexplosive alternative methods to conventional rock blasting. Open Geosciences, 13(1), 431-442.
- Hagan, T. N. (1980). Rock breakage by explosives. In Gasdynamics of Explosions and Reactive Systems(pp. 329-340). Pergamon.
- Banadaki, M. M. D. (2010). Stress-wave induced fracture in rock due to explosive action(p. 128). Toronto: Univerity of Toronto.
- Djordjevic, N. (2013). Efficiency of conversion of explosives energy into rock fragmentation. AusIMM Bulletin, (6).
- Davison, L., Horie, Y., & Graham, R. A. (2008). Shock Wave and High Pressure Phenomena(Vol. 111). Springer-Verlag, Berlin.
- Ma, S., Liu, K., & Yang, J. (2024). Investigation of blast-induced rock fragmentation and fracture characteristics with different decoupled charge structures. International Journal of Impact Engineering, 185, 104855.
- Morley, C. K., Von Hagke, C., Hansberry, R., Collins, A., Kanitpanyacharoen, W., & King, R. (2018). Review of major shale-dominated detachment and thrust characteristics in the diagenetic zone: Part II, rock mechanics and microscopic scale. Earth-Science Reviews, 176, 19-50.
- Mandal, S. K., Singh, M. M., & Dasgupta, S. (2008). Theoretical concept to understand plan and design smooth blasting pattern. Geotechnical and Geological Engineering, 26(4), 399-416.
- Choudhary, B. S., Sonu, K., Kishore, K., & Anwar, S. (2016). Effect of rock mass properties on blast-induced rock fragmentation. International Journal of Mining and Mineral Engineering, 7(2), 89-101.
- Roy, M. P., Paswan, R. K., Sarim, M. D., Kumar, S., Jha, R., & Singh, P. K. (2016). Rock fragmentation by blasting-A review. Journal of mines, metals and fuels, 64(9), 424-431.
- Hook, J. R. (2003). An introduction to porosity. Petrophysics-The SPWLA Journal of Formation Evaluation and Reservoir Description, 44(03).
- Mulenga, S. (2020). Evaluation of factors influencing rock fragmentation by blasting using interrelations diagram method. Journal of Physical Sciences, 2(1), 1-15.
- Alipour, A., & Asadizadeh, M. (2023). Rock fragment size prediction using RSM in bench blasting: a focus on the influencing factors and their interactions. Arabian Journal of Geosciences, 16(1), 61.
- Agrawal, A., Choudhary, B. S., Murthy, V. M. S. R., & Murmu, S. (2022). Impact of bedding planes, delay interval and firing orientation on blast induced ground vibration in production blasting with controlling strategies. Measurement, 202, 111887.
- McKenzie, C. K. (2013). Limits blast design: Controlling vibration, gas pressure & fragmentation. In Rock Fragmentation by Blasting: The 10th International Symposium on Rock Fragmentation by Blasting, 2012 (Fragblast 10)(pp. 85-94). Taylor & Francis Books Ltd.
- Babaei Khorzoughi, M. (2013). Use of measurement while drilling techniques for improved rock mass characterization in open-pit mines(Doctoral dissertation, University of British Columbia).
- Monjezi, M., Rezaei, M., & Varjani, A. Y. (2009). Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. International Journal of Rock Mechanics and Mining Sciences, 46(8), 1273-1280.
- https://doi.org/10.1016/j.ijrmms.2009.05.005
- Rezaei, M. (2020). Feasibility of novel techniques to predict the elastic modulus of rocks based on the laboratory data. International Journal of Geotechnical Engineering.
- Majdi, A., & Rezaei, M. (2013, June). Application of artificial neural networks for predicting the height of destressed zone above the mined panel in longwall coal mining. In ARMA US Rock Mechanics/Geomechanics Symposium(pp. ARMA-2013). ARMA. https://onepetro.org/ARMAUSRMS/proceedings-abstract/ARMA13/All-ARMA13/121153
- Davoodi, P. K., Hajizadeh, F., & Rezaei, M. (2025). Evaluating the RMR correlation with the rock mass wave velocity using the meta-heuristics algorithms. Scientific Reports, 15(1), 17716.
- Wang, Y., Rezaei, M., Abdullah, R. A., & Hasanipanah, M. (2023). Developing two hybrid algorithms for predicting the elastic modulus of intact rocks. Sustainability, 15(5), 4230.
- Rezaei, M., Habibi, H., & Asadizadeh, M. (2024). Determination of the stress concentration factor adjacent an extracted underground coal panel using the CART and MARS algorithms. Earth Science Informatics, 17(6), 5733-5750.
- Rezaei, M., & Rajabi, M. (2021). Assessment of plastic zones surrounding the power station cavern using numerical, fuzzy and statistical models. Engineering with Computers, 37(2), 1499-1518.
- MolaAbasi, H., Khajeh, A., & Jamshidi Chenari, R. (2021). Use of GMDH-type neural network to model the mechanical behavior of a cement-treated sand. Neural Computing and Applications, 33(22), 15305-15318. Asadizadeh, M., & Rezaei, M. (2021). Surveying the mechanical response of non-persistent jointed slabs subjected to compressive axial loading utilising GEP approach. International Journal of Geotechnical Engineering.
- Khoshjavan, S., Mazlumi, M., Rezai, B., & Rezai, M. (2010). Estimation of hardgrove grindability index (HGI) based on the coal chemical properties using artifical neural networks. Oriental Journal of Chemistry, 26(4), 1271. http://www.orientjchem.org/?p=11608
- Nwaila, G. T., Frimmel, H. E., Zhang, S. E., Bourdeau, J. E., Tolmay, L. C., Durrheim, R. J., & Ghorbani, Y. (2022). The minerals industry in the era of digital transition: An energy-efficient and environmentally conscious approach. Resources Policy, 78, 102851.
- Kim, M., Ismail, L. A., & Kwon, S. (2021). Review of the application of artificial intelligence in blasting area. Explosives and Blasting, 39(3), 44-64.
- Baghirli, O. (2015). Comparison of Lavenberg-Marquardt, scaled conjugate gradient and Bayesian regularization backpropagation algorithms for multistep ahead wind speed forecasting using multilayer perceptron feedforward neural network.c
- Taiwo, B. O. (2022). Improvement of small-scale dolomite blasting productivity: comparison of existing empirical models with image analysis software and artificial neural network models. Journal of Mining and Environment, 13(3), 627-641.
- Ghasemi, E., Sari, M., & Ataei, M. (2012). Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines. International Journal of Rock Mechanics and Mining Sciences, 52, 163-170.
- Ouchterlony, F., & Sanchidrián, J. A. (2018). A review of the development of better prediction equations for blast fragmentation. Rock Dynamics and Applications 3, 25-45.
- Hosseini, S., Pourmirzaee, R., Armaghani, D. J., & Sabri Sabri, M. M. (2023). Prediction of ground vibration due to mine blasting in a surface lead–zinc mine using machine learning ensemble techniques. Scientific Reports, 13(1), 6591.
- Fissha, Y., Ikeda, H., Toriya, H., Adachi, T., & Kawamura, Y. (2023). Application of Bayesian Neural Network (BNN) for the prediction of blast-induced ground Applied Sciences, 13(5), 3128.
- Taiwo, F. (2016). The case for the median fragment size as a better fragment size descriptor than the mean. Rock mechanics and rock engineering, 49(1), 143-164. Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation(pp. 4661-4667). IEEE.
- Khajooei Sirjani, A., Sereshki, F., Ataei, M.., and Hosseini, M.A. (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), Archives of Mining Sciences, DOI:10.24425/ams.2022.140705, 67 (2022), 1, 107-121
- Ataei, M., and Sereshki, F. (2017). Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm, Journal of Mining and Environment (JME), DOI: 10.22044/jme.2016.654, Vol. 8, No.2, PP. 291-304.
- Ghasemi, E., Amini, H., Ataei, M., and Khalokakaei, R. (2014). Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation, Arabian Journal of Geosciences, January 2014, Volume 7, Issue 1, PP 193-202.
- Mohammadi S., Ataei M., Khalokakaei R., and Pourzamani E. (2015) Comparison of golden section search method and imperialist competitive algorithm for optimization cut-off grade- case study: Mine No. 1 of Golgohar, Journal of Mining and Environment (JME), Vol. 6, No.1, PP.63-71.
- Rafiee, R., Ataei, M., and Azarfar, A. (2016). Determination of optimum cutoff grades to maximize net present value by using Imperialism Competitive Algorithm (ICA), Journal of Analytical and Numerical Methods in Mining Engineering, Volume 6, Issue 11 - Serial Number 11, July 2016, Pages 89-99.
- Mohammadi, S., Khalokakaei, R., Ataei, M., and Pourzamani E. (2017). Determination of the optimum cut-off grades and production scheduling in multi-product open pit mines using imperialist competitive algorithm (ICA), Resources Policy, DOI: 10.1016/j.resourpol.2016.11.005, 51 (2017), PP. 39–48.
- Mikaeil R., Shaffiee Haghshenas Sina, Shaffiee Haghshenas Sami, and Ataei M. (2018). Performance Prediction of Circular Saw Machine Using Imperialist Competitive Algorithmand Fuzzy Clustering Technique, Neural Computing and Applications, DOI: 10.1007/s00521-016-2557-4, March 2018, Volume 29, Issue 6, PP. 283–292.
- Saeidi O., Torabi S.R., and Ataei M. (2014). Prediction of the Rock Mass Diggability Index by Using Fuzzy Clustering-Based, ANN and Multiple Regression Methods, Rock Mechanics and Rock Engineering, March 2014, Volume 47, Issue 2, PP 717-732.
- Hoseini S.M., Sereshki F., and Ataei M. (2016). Blast Fragmentation Measurement Using Image Processing, Int. Journal of Mining and Geo-Engineering (IJMGE), VOL.50, NO.2, PP.211–218.
- Sari M., Ghasemi E., and Ataei M. (2014). Stochastic Modeling Approach for the Evaluation of Backbreak due to Blasting Operations in Open Pit Mines, Rock Mechanics and Rock Engineering, March 2014, Volume 47, Issue 2, PP 771-783.
- Motahedi, A., Sereshki, F., and Ataei M. (2018). Development of overbreak prediction models in drill and blast tunneling by using soft computing methods, Engineering with Computers, DOI 10.1007/s00366-017-0520-3.January 2018, Volume 34, Issue 1, PP. 45–58.
- Davarzani, M.R., Ataei, M., and Sereshki F. (2024). Flyrock Risk Assessment in Blasting Operations of Road Construction in Hard Rock using the FFTA-FDAHP Method, Iranian Journal of Earth Sciences, 17 (2), 1-18.
- Taiwo, B. O., & Adebayo, B. (2023). Improvement of blast-induced fragmentation using artificial neural network and blastFrag© optimizer software. Materials and Geoenvironment, 69(1), 1-13.
- Taiwo, B. O., Shahani, N. M., Omosebi, A., Samson, O. B., & Akinlabi, A. A. (2024). Development of mathematically motivated artificial intelligence models for the prediction of carbonate rock lime saturation factor for cement production. Engineering Applications of Artificial Intelligence, 127, 107444.
- Weidong, L. I., Suhayb, M. K., Thangavelu, L., Marhoon, H. A., Pustokhina, I., Alqsair, U. F., ... & Alashwal, M. (2022). Implementation of AdaBoost and genetic algorithm machine learning models in prediction of adsorption capacity of nanocomposite materials. Journal of Molecular Liquids, 350, 118527.
- Jasir, M. P., Balakrishnan, K., & Jaseena, K. U. (2022). Random forest and AdaBoost-DT: Ensemble machine learning Estimators to model Malayalam poem syllable duration. In Soft Computing: Theories and Applications: Proceedings of SoCTA 2021(pp. 355-365). Singapore: Springer Nature Singapore.
- Wang, Y., & Feng, L. (2020). Improved Adaboost algorithm for classification based on noise confidence degree and weighted feature selection. IEEE Access, 8, 153011-153026.
- Ballabio, C., & Sterlacchini, S. (2012). Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Mathematical geosciences, 44, 47-70.
- Deka, P. C. (2014). Support vector machine applications in the field of hydrology: a review. Applied soft computing, 19, 372-386.
- Zhou, J., Zhang, Y., & Qiu, Y. (2024). State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting. Artificial Intelligence Review, 57(1), 1-54.
- Toochaei, M. R., & Moeini, F. (2023). Evaluating the performance of ensemble classifiers in stock returns prediction using effective features. Expert Systems with Applications, 213, 119186.
- Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
- Wang, Y., Cui, G., & Xu, J. (2020). Semi-automatic detection of buried rebar in GPR data using a genetic algorithm. Automation in Construction, 114, 103186.
- Elbeltagi, A., Pande, C. B., Kumar, M., Tolche, A. D., Singh, S. K., Kumar, A., & Vishwakarma, D. K. (2023). Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models. Environmental Science and Pollution Research, 30(15), 43183-43202.
- Khatti, J., & Grover, K. S. (2023). Prediction of UCS of fine-grained soil based on machine learning part 2: comparison between hybrid relevance vector machine and Gaussian process regression. Multiscale and Multidisciplinary Modeling, Experiments and Design, 1-41.
- Hosseini, S., Khandakar, A., Chowdhury, M. E., Ayari, M. A., Rahman, T., Chowdhury, M. H., & Vaferi, B. (2022). Novel and robust machine learning approach for estimating the fouling factor in heat exchangers. Energy Reports, 8, 8767-8776.
)