A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock Distance Prediction
Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock… R. Zhang et al.
In the field of civil and mining engineering, blasting operations are widely and frequently used for rock excavation, However, some undesirable environmental problems induced by blasting operations cannot be ignored. Blast-induced flyrock is one important issue induced by blasting operation, which needs to be well predicted to identify the blasting zone’s safety zone. This study introduces an adaptive weighted multi-kernel learning model (AW-MKL) to provide an accurate prediction of blast-induced flyrock distance in Sungun Copper Mine site. The proposed model uses a combination of multi-kernel learning (MKL) approach and adaptive weighting strategy based on weighted Euclidean distance and modified local outlier factor (MLOF) to maximally improve the predictive ability of kernel ridge regression (KRR). To demonstrate the superiority of the proposed approach, six machine learning models were developed as comparisons, i.e., KRR, RF, GBDT, SVM, M5 Tree, MARS and AdaBoost. The outcomes of the proposed method achieved the highest accuracy in testing phase, with RMSE of 2.05, MAE of 0.98 and VAF of 99.92, which confirmed the strong predictive capability of the proposed AW-MKL in predicting blast-induced flyrock distance.
An adaptive weighted multi-kernel learning model (AW-MKL) to predict blast induced flyrock.
Improve the performance of kernel ridge regression (KRR) using the combination of multi-kernel learning (MKL) and adaptive weighting.
Use MKL to reduce the effort devoted in determine optimal kernel.
Measure the correlation between model input and training samples using weighted Euclidean distance.
Propose a modified local outlier factor (MLOF) to evaluate the uncertainty of training samples.
The proposed adaptive weighting strategy assign weights to training samples according to the weighted Euclidean distance and MLOF.
Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock Distance Prediction
Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock… R. Zhang et al.
In the field of civil and mining engineering, blasting operations are widely and frequently used for rock excavation, However, some undesirable environmental problems induced by blasting operations cannot be ignored. Blast-induced flyrock is one important issue induced by blasting operation, which needs to be well predicted to identify the blasting zone’s safety zone. This study introduces an adaptive weighted multi-kernel learning model (AW-MKL) to provide an accurate prediction of blast-induced flyrock distance in Sungun Copper Mine site. The proposed model uses a combination of multi-kernel learning (MKL) approach and adaptive weighting strategy based on weighted Euclidean distance and modified local outlier factor (MLOF) to maximally improve the predictive ability of kernel ridge regression (KRR). To demonstrate the superiority of the proposed approach, six machine learning models were developed as comparisons, i.e., KRR, RF, GBDT, SVM, M5 Tree, MARS and AdaBoost. The outcomes of the proposed method achieved the highest accuracy in testing phase, with RMSE of 2.05, MAE of 0.98 and VAF of 99.92, which confirmed the strong predictive capability of the proposed AW-MKL in predicting blast-induced flyrock distance.
An adaptive weighted multi-kernel learning model (AW-MKL) to predict blast induced flyrock.
Improve the performance of kernel ridge regression (KRR) using the combination of multi-kernel learning (MKL) and adaptive weighting.
Use MKL to reduce the effort devoted in determine optimal kernel.
Measure the correlation between model input and training samples using weighted Euclidean distance.
Propose a modified local outlier factor (MLOF) to evaluate the uncertainty of training samples.
The proposed adaptive weighting strategy assign weights to training samples according to the weighted Euclidean distance and MLOF.
Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock Distance Prediction
Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock… R. Zhang et al.
Rock Mech Rock Eng
Zhang, Ruixuan (author) / Li, Yuefeng (author) / Gui, Yilin (author) / Armaghani, Danial Jahed (author) / Yari, Mojtaba (author)
Rock Mechanics and Rock Engineering ; 58 ; 679-695
2025-01-01
17 pages
Article (Journal)
Electronic Resource
English
Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock Distance Prediction
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