Open Access

REVIEW

Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

Mojtaba Yari^1,*, Manoj Khandelwal², Payam Abbasi³, Evangelos I. Koutras⁴, Danial Jahed Armaghani^5,*, Panagiotis G. Asteris⁴

1 Department of Mining Engineering, Faculty of Engineering, Malayer University, Malayer, 9586365719, Iran
2 Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3350, Australia
3Marane Olia Ornamental Stone Mine, Sanandaj, 6614344588, Iran
4 Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, 15122, Greece
5 School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia

* Corresponding Authors: Mojtaba Yari. Email: ; Danial Jahed Armaghani. Email:

Computer Modeling in Engineering & Sciences 2024, 140(3), 2207-2238. https://doi.org/10.32604/cmes.2024.048071

Received 27 November 2023; Accepted 17 May 2024; Issue published 08 July 2024

Abstract

Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to the unintended fracturing of rock beyond the desired blast perimeter, can significantly impact project timelines and costs. This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations, specifically focusing on backbreak prediction. The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects.

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