Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
Source abstract
Abstract
Blasting operations involve some undesirable environmental issues that may cause damage to equipment and surrounding areas. One of them, and probably the most important one, is flyrock induced by blasting, where its accurate estimation before the operation is essential to identify the blasting zone’s safety zone. This study introduces several tree-based solutions for an accurate prediction of flyrock. This has been done using four techniques, i.e., decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). The modelling of tree-based techniques was conducted with in-depth knowledge and understanding of their most influential factors. The mentioned factors were designed through the use of several parametric investigations, which can also be utilized in other engineering fields. As a result, all four tree-based models are capable enough for blasting-induced flyrock prediction. However, the most accurate predicted flyrock values were obtained using the AdaBoost technique. Observed and forecasted flyrock by AdaBoost for the training and testing phases received coefficients of determination (R2) of 0.99 and 0.99, respectively, which confirm the power of this technique in estimating flyrock. Additionally, according to the results of the input parameters, the powder factor had the highest influence on flyrock, whereas burden and spacing had the lowest impact on flyrock.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Rock Mechanics and Modeling · Landslides and related hazards · Tunneling and Rock Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mining Engineering, Faculty of Engineering, Malayer University, Malayer 65719-95863, Iran
Centre of Tropical Geoengineering (GEOTROPIK), Institute of Smart Infrastructure and Innovative Engineering (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76 Lenin Prospect, 454080 Chelyabinsk, Russia
Department of Natural Resources Management & Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Athens, Greece
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