Affiliation:
1. Indian Institute of Technology Dhanbad
2. Malla Reddy Engineering College
3. Aksum University
Abstract
Abstract
In the current study, two algorithms, custom XG Boost (CXGBA) and improved genetic XG-Boost algorithm (IGXGBA) have been chosen to create an empirical formula for the simultaneous prediction of the mean fragmentation size (MFS) and the peak particle velocity (PPV) with sourced data sets of geo-blast parameters such as spacing burden ratio (S/B), stemming length (T), decking length (DL), firing pattern (FP), total quantity of explosive (TE), maximum charge per delay (MCD), measuring distance (MD), joint angle (JA), joint spanning height(JSP), joint set number (Jn), and rock compressive strength. Advanced technical combinations like K-10 cross-validation, and grid search executed along genetic algorithm processes with a high mutation rate to XGBoost algorithm. All algorithms were executed using Python programming in the Google Colab platform. The results unveiled that IGXGBA is superior and effective in-terms of metric R 2 , RMSE and MAPE in predicting MFS & PPV. A WEB APP called Bhanwar Blasting Formula (BBF) was created utilizing Google Cloud Platform (GCP) and FLASK APP to benefit practicing mining engineers to predict blasting results easily from the site itself, and identify optimization.
Publisher
Research Square Platform LLC
Reference44 articles.
1. Zhu Z, Xie H, And Mohanty B (2008) Numerical investigation of blasting induced damage in cylindrical rocks, vol 44. International Journal of Rock Mechanics and Mining Sciences, pp 111–121
2. Modelling the impact and blast design variation on blast fragmentation;Thornton D;Int J Blasting Fragmentation,2002
3. An Investigation into the Effect of Rockmass Properties on Mean Fragmentation;Sri Chandrahas N;Arch Min Sci,2021
4. Effect of rock mass properties on blast-induced rock fragmentation;Choudhary BS;Int J Min Miner Eng,2016
5. Artificial intelligence is almost here;Halal WE;On the Horizon,2003