A stacked deep multi-kernel learning framework for blast induced flyrock prediction

• Published in: International journal of rock mechanics and mining sciences (Oxford, England : 1997) England : 1997), 2024-05, Vol.177, p.105741, Article 105741
• Main Authors: Zhang, Ruixuan, Li, Yuefeng, Gui, Yilin, Armaghani, Danial Jahed, Yari, Mojtaba
• Format: Article
• Language: English
• Subjects: Deep learning; Flyrock prediction; Gradient boosting; Multi-feature fusion; Multi-kernel learning; Stacked-representation learning
• ISSN: 1365-1609; 1873-4545
• DOI: 10.1016/j.ijrmms.2024.105741

Blasting operations are widely and frequently used for rock excavation in Civil and Mining constructions. Flyrock is one of the most important issues induced by blasting operations in open pit mines, and therefore needs to be well predicted in order to identify the safety zone to prevent the potential injuries. For this purpose, 234 sets of blasting data were collected from Sungun Copper Mine site, and a stacked deep multi-kernel learning (SD-MKL) framework was proposed to estimate the blast induced flyrock with confidence accuracy. The proposed model uses the stacking-based representation learning framework (S-RL) to achieve deep learning on small-scale training sets. A multi-kernel learning model (MKL) is used as the base module of S-RL framework, which uses a multi-feature fusion strategy to generate multiple kernels with different kernel length in order to reduce the effort in tuning hyperparameters. In addition, this study further enhanced the predictive capability of SD-MKL by introducing the boosting method into the S-RL framework and hence proposed a boosted SD-MKL model. For comparison purpose, several existing machine learning models were implemented, i.e., kernel ridge regression (KRR), support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), ensemble deep random vector functional link (edRVFL), SD-KRR and SD-SVM. Our experimental results showed that the proposed boosted SD-MKL achieved the best overall performance, with the lowest RMSE of 0.21/1.73, MAE of 0.08/0.78, and the highest VAF of 99.98/99.24. •A stacked deep learning multi-kernel learning model (SD-MKL) for flyrock prediction.•Use stacked-representation learning (S-RL) framework to achieve deep learning.•Use the multi-kernel learning model with multi-feature fusion strategy to learn the relationship between generated feature and original feature.•Use boosting method to improve the prediction accuracy of SD-MKL.