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Blast-induced ground vibration prediction in granite quarries: An application of gene expression programming, ANFIS, and sine cosine algorithm optimized ANN
Department of Energy Resources Engineering, Inha University, Incheon 22212, Republic of Korea. Department of Mining Engineering, Federal University of Technology, Akure, Nigeria.
Department of Energy Resources Engineering, Inha University, Incheon 22212, Republic of Korea.
Department of Physics, Federal University, Oye-Ekiti, Nigeria.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering. Department of Mining Engineering, Federal University of Technology, Akure, Nigeria.ORCID iD: 0000-0002-3838-8472
2021 (English)In: International Journal of Mining Science and Technology, ISSN 2095-2686, Vol. 31, no 2, p. 265-277Article in journal (Refereed) Published
Abstract [en]

Blasting of rocks has intrinsic environmental impacts such as ground vibration, which can interfere with the safety of lives and property. Hence, accurate prediction of the environmental impacts of blasting is imperative as the empirical models are not accurate as evident in the literature. Therefore, there is need to consider some robust predictive models for accurate prediction results. Gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and sine cosine algorithm optimized artificial neural network (SCA-ANN) models are proposed for predicting the blast-initiated ground vibration in five granite quarries. The input parameters into the models are the distance from the point of blasting to the point of measurement (D), the weight of charge per delay (W), rock density (ρ), and the Schmidt rebound hardness (SRH) value while peak particle velocity (PPV) is the targeted output. 100 datasets were used in developing the proposed models. The performance of the proposed models was examined using the coefficient of determination (R2) and error analysis. The R2 values obtained for the GEP, ANFIS, and SCA-ANN models are 0.989, 0.997, and 0.999, respectively, while their errors are close to zero. The proposed models are compared with an empirical model and are found to outperform the empirical model.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 31, no 2, p. 265-277
Keywords [en]
Artificial intelligence, Blasting, Rock density, Comminution, Environmental impacts, Sensitivity analysis
National Category
Mineral and Mine Engineering
Research subject
Mining and Rock Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-83001DOI: 10.1016/j.ijmst.2021.01.007ISI: 000623878300003Scopus ID: 2-s2.0-85101019372OAI: oai:DiVA.org:ltu-83001DiVA, id: diva2:1529422
Note

Validerad;2021;Nivå 2;2021-03-02 (alebob);

Finansiär: Ministry of Science, Korea; ICT (2019H1D3A1A01102993)

Available from: 2021-02-18 Created: 2021-02-18 Last updated: 2021-04-06Bibliographically approved

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Idris, Musa Adebayo

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