A stacked deep multi-kernel learning framework for blast induced flyrock prediction
Open access copy at publisher website
Description
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.
Impact and interest:
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ID Code: | 248030 | ||||
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Item Type: | Contribution to Journal (Journal Article) | ||||
Refereed: | Yes | ||||
ORCID iD: |
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Measurements or Duration: | 14 pages | ||||
DOI: | 10.1016/j.ijrmms.2024.105741 | ||||
ISSN: | 1365-1609 | ||||
Pure ID: | 166907234 | ||||
Divisions: | Current > Research Centres > Centre for Materials Science Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Computer Science Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Civil & Environmental Engineering |
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Funding Information: | The first author (R. Zhang) also would like to thank the financial support from Queensland University of Technology and the China Scholarship Council, on this study. | ||||
Copyright Owner: | 2024 The Author(s) | ||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||
Deposited On: | 19 Jun 2024 05:52 | ||||
Last Modified: | 19 Jun 2024 05:52 |
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