Journal
NATURAL RESOURCES RESEARCH
Volume 29, Issue 2, Pages 593-607Publisher
SPRINGER
DOI: 10.1007/s11053-019-09575-5
Keywords
Genetic algorithm; Cubist algorithm; GA-CA model; Air overpressure; Quarry mine
Categories
Funding
- Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam
- Duy Tan University, Da Nang, Vietnam
- Center for Mining, Electro-Mechanical Research of HUMG
Ask authors/readers for more resources
In the present work, blast-induced air overpressure is estimated by an innovative intelligence system based on the cubist algorithm (CA) and genetic algorithm (GA) with high accuracy, called GA-CA model. Herein, CA initialization model was developed first and the hyper-parameters of the CA model were selected randomly. Subsequently, the GA procedure was applied to perform a global search for the optimized values of the hyper-factors of the CA model. Root-mean-square error (RMSE) is utilized as a compatibility function to determine the optimal CA model with the lowest RMSE. Gaussian process (GP), conditional inference tree (CIT), principal component analysis (PCA), hybrid neural fuzzy inference system (HYFIS) and k-nearest neighbor (k-NN) models are also developed as the benchmark models in order to compare and analyze the quality of the proposed GA-CA algorithm; 164 blasting works were investigated at a quarry mine of Vietnam for this aim. The results revealed that GA significantly improved the performance of the CA model. Based on the statistical indices used for model assessment, the proposed GA-CA model was confirmed as the most superior model as compared to the other models (i.e., GP, CIT, HYFIS, PCA, k-NN). It can be applied as a robust soft computing tool for estimating blast-induced air overpressure.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available