Document Type : Original Research Paper


1 Department of Mining Engineering, Federal University of Technology, Akure, Nigeria

2 Department of Mining Engineering, West Virginia University, USA

3 Mineral Economics Lab, Department of Mining Engineering, Federal University of Technology, Akure, Nigeria

4 Mining Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia

5 Department of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Japan

6 Department of Mining Engineering, Aksum University, Aksum 7080, Tigray, Ethiopia

7 University of Bolton, England

8 Mines Department, Dangote Cement Plc, Ibese, Nigeria


The purpose of this research work is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo State, aggregate quarries. In addition, an Artificial Neural Network (ANN) model for granite profitability was developed. A structured survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. In this study, the efficacy of granite fragmentation was assessed using the WipFrag software. The relationship between particle size distribution, blast design, blast efficiency, and uniformity index were analyzed using the WipFrag result. The optimum blast design was also identified and recommended for mine production. The result revealed that large burden distances result in bigger X50, X80, and Xmax fragmentation sizes. A burden distance of 2 m and a 2 m spacing were identified as the optimum burden and spacing. The finding revealed that blast mean size and 80% passing mesh size have a positive correlation. The result from this study indicated that the uniformity index has a positive correlation with blast efficiency and a negative correlation with maximum blast fragmentation size. The prediction accuracy of the developed models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and mean square error (MSE). The error analysis revealed that the ANN model is suitable for predicting quarry-generated profit.


Main Subjects

[1]. Osasan S.K. (2009). Economic Assessment of Granite Quarrying in Oyo State, Nigeria. Journal of Engineering and Applied Sciences, Vol. 4(2), pp.135 – 140.
[2]. Hirooka, M. (2006). Innovation dynamism and economic growth: A non-linear perspective. Edward Elgar Publishing.
[3]. Matthew, O. O. and Emmanuel, I. E. (2013). Solid Minerals Development in Parts of Southwest Nigeria-in the Light of Recent Reforms. British Journal of Applied Science & Technology, 3(4), 1391.
[4]. United States Geological Survey (USGS). Available at:; accessed 20th April, 2018.
[5]. Petters S.W. (1991). Precambrian Geology of Africa. Lecture notes in Earth Sciences. 40p. Springer Berlin, Heidelberg.
[6]. Akabzaa, T. and Darimani, A. (2001). Impact of mining sector investment in Ghana: A study of the Tarkwa mining region. Third World Network, 11(2), 47-61.
[7]. Metal and Economics Group (MEG) (2011). Worldwide Exploration Trends: Special report from Metals Economics Group for the PDAC International Convention. 8pp.
[8]. Melodi, M. M., Taiwo, B. O., and Ajayi, I. O. (2022). Evaluation of Granite Production and Market Structure for the Improvement of Sales Performance in Ondo and Ogun States, Southwest Nigeria.
[9]. Cornelius, N., Amujo, O., and Pezet, E. (2019). British ‘Colonial governmentality’: slave, forced and waged worker policies in colonial Nigeria, 1896–1930. Management & Organizational History, 14(1), 10-32.
[10]. Yemi, O. (2005). Financing solid minerals business in Nigeria: an appraisal of the socio-political aspects of the requirements of bankability; Legal aspects of finance in emerging markets; 107-118p.
[11]. Feely, K. C. and Christensen, P. R. (1999). Quantitative compositional analysis using thermal emission spectroscopy: Application to igneous and metamorphic rocks. Journal of Geophysical Research: Planets, 104(E10), 24195-24210.
[12]. Haldar, S.K. and Josip, T. (2014). in Introduction to Mineralogy and Petrology, Geotech GeolEng39, pp. 1715–1726
[13]. Kosmatka, S. H., Panarese, W. C., and Kerkhoff, B. (2002). Design and control of concrete mixtures (Vol. 5420, pp. 60077-1083). Skokie, IL: Portland Cement Association.
[14]. Eyre, J. M. and Agba, A. V. (2007). Nigeria-An Economic Analysis of Natural Resources Sustainability for the Mining Sector Component.
[15]. Bamgbose, T. O., Omisore, O. A., Ademola, A. O., and Oyesola, O. B. (2014). Challenges of quarry activities among rural dwellers in Odeda local government area of Ogun state. Research Journal of Agricultural and Environmental Sciences, 3(1), 49-55.
[16]. Adeyi, G. O., Mbagwu, C. C., Ndupu, C. N., and Okeke, O. C. (2019). Production and uses of crushed rock aggregates: an overview. International Journal of Advanced Academic Research, Sciences, Technology and Engineering, 5(8), 92-110.
[17]. Saliu, M.A and Haleem, J.O. (2012). Investigations into Aesthetic properties of Selected Granite in South Western Nigeria as Dimension Stone, Journal of Engineering Science and Technology Vol. 7, No. 4, pp. 418-419.
[18]. Frank, A., Kolapo, P., Ogunsola, N., Munemo, P., and Akinola, A. (2022). Application of Improved Blasting Techniques in Open Pit Mining for Maximum Productivity: A Case of Oakyam Quarry Limited, Ogun State, Nigeria. Science10(2), 12-23.
[19]. Taiwo, B. O., Yewuhalashet, F., Adamolekun, L. B., Bidemi, O. O., Famobuwa, O. V., and Victoria, A. O. (2023). Development of artificial neural network based mathematical models for predicting small scale quarry powder factor for efficient fragmentation coupled with uniformity index model. Artificial Intelligence Review, 1-22.
[20]. Morin, M. A. and Ficarazzo, F. (2006). Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz–Ram model. Computers & geosciences32(3), 352-359.
[21]. Singh, P. K., Roy, M. P., Paswan, R. K., Sarim, M. D., Kumar, S., and Jha, R. R. (2016). Rock fragmentation control in opencast blasting. Journal of Rock Mechanics and Geotechnical Engineering8(2), 225-237.
[22]. Chung, S. H. and Katsabanis, P. D. (2000). Fragmentation prediction using improved engineering formulae. Fragblast4(3-4), 198-207.
[23]. Cunningham, C. V. B. (2005). The Kuz-Ram fragmentation model–20 years on. In Brighton conference proceedings (Vol. 4, pp. 201-210). European Federation of Explosives Engineers.
[24]. Sereshki, F., Ataei, M., and Hoseinie, S. H. (2010). Comparison and analysis of burden design methods in blasting: a case study on Sungun copper mine in Iran. International Journal of Mining and Mineral Engineering2(2), 123-136.
[25]. Kahraman, E. and Kilic, A. M. (2020). Evaluation of empirical approaches in estimating mean particle size after blasting by using nondestructive methods. Arabian Journal of Geosciences13(14), 613.
[26]. Hosseini, S., Poormirzaee, R., Hajihassani, M., and Kalatehjari, R. (2022). An ANN-fuzzy cognitive map-based Z-number theory to predict flyrock induced by blasting in open-pit mines. Rock Mechanics and Rock Engineering55(7), 4373-4390.
[27]. Hosseini, S., Khatti, J., Taiwo, B. O., Fissha, Y., Grover, K. S., Ikeda, H., ... and Ali, M. (2023). Assessment of the ground vibration during blasting in mining projects using different computational approaches. Scientific Reports13(1), 18582.
[28]. 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 Geosciences7, 193-202.
[29]. Mishra, A. K. and Sinha, M. (2015). Environmental impact analysis of heavy blasting in large opencast mines. Journal of Mines, Metals & Fuels63(7).
[30]. Odeyemi, O. Y., Taiwo, B. O., and Alaba, O. (2023). Influence of explosive maximum instantaneous charge on blasting environmental impact. Journal of Sustainable Mining22(4), 343.
[31]. Manashti, J., Pirnia, P., Manashty, A., Ujan, S., Toews, M., and Duhaime, F. (2023). PSDNet: Determination of Particle Size Distributions using Synthetic Soil Images and Convolutional Neural Networks. arXiv preprint arXiv:2303.04269.
[32]. Amin, I. and Salman, S. (2022). Fragmentation Analysis of Blasted Rock using WipFrag Image Analysis Software. Journal of Mines, Metals and Fuels70(5), 171-181.
[33]. Maerz, N. H., Palangio, T. C., and Franklin, J. A. (2018). WipFrag image based granulometry system. In Measurement of Blast Fragmentation (pp. 91-99). Routledge.
[34]. Nanda, S. and Pal, B. K. (2020). Analysis of blast fragmentation using WipFrag. J Image5(6).
[35]. Taiwo, B. O., Angesom, G., Fissha, Y., Kide, Y., Li, E., Haile, K., and Oni, O. A. (2023). Artificial neural network modeling as an approach to Limestone blast production rate prediction: A comparison of PI-BANN, and MVR models. Journal of Mining and Environment.
[36]. Namin, F. S., Ghadi, A., and Saki, F. (2022). A literature review of Multi Criteria Decision-Making (MCDM) towards mining method selection (MMS). Resources Policy77, 102676.
[37]. Doktan, M. (2001). Impact of Blast Fragmentation on Truck Shovel Fleet Performance. Paper presented at the 17th International Mining Congress and Exhibition of Turkey - IMCET2001.
[38]. Osanloo, M. and Hekmat, A. (2005). Prediction of shovel productivity in the Gol-e-Gohar iron mine. Journal of Mining science, 41(2), 177-184.
[39]. Ogungbe, M. A. (2018). Effect of Indiscriminate Industrial Waste Disposal on the Health of the Industrial Layout’s Resident, Akure, Ondo State. AASCIT Journal of Health, 5(2), 39-45.
[40]. Oluyede, O. K., Garba, I., Danbatta, U., Ogunleye, P., and Klötzli, U. (2020). Field occurrence, petrography and structural characteristics of basement rocks of the northern part of Kushaka and Birnin Gwari schist belts, northwestern Nigeria. Journal of Natural Sciences Research. ISSN (Paper), 2224-3186.
[41]. Gerald, B. (2018). A brief review of independent, dependent and one sample t-test. International Journal of Applied Mathematics and Theoretical Physics, 4(2), 50-54.
[42]. MMSD (2022). Review of Royalty Rates for Mineral Production in Nigeria MSMD/MID/OP/1346/I/13Retrieve from:
[43]. Neaupane, K. M. and Adhikari, N. R. (2006). Prediction of Tunneling-Induced Ground Movement with the Multi-Layer Perceptron. Tunnelling and Underground Space Technology 21.2, 151-159.
[44]. Jug, J., Strelec, S., Gazdek, M., and Kavur, B. (2017). Fragment size distribution of blasted rock mass. In IOP Conference series: earth and environmental science (Vol. 95, No. 4, p. 042013). IOP Publishing.
[45]. Jalil, K. and Raza, S. (2019). Cost Estimation for Bench Drilling Phase of Diamond Wire Sawing Technique for Granite Mining. U: International Journal of Scientific and Research Publifications, 9(3), 455-463.