Enhancing the Performance of LSSVM Model in Predicting Rock Fragmentation Size Via Optimization Algorithms-Reference-Cited by-同舟云学术

Enhancing the Performance of LSSVM Model in Predicting Rock Fragmentation Size Via Optimization Algorithms

Published:2023-07-22 Issue:9 Volume:27 Page:3765-3777
ISSN:1226-7988
Container-title:KSCE Journal of Civil Engineering
language:en
Short-container-title:KSCE J Civ Eng

Author:

Zheng Haowen^ORCID,Liu Jun^ORCID,Zhuang Ruihong^ORCID,Zhao Futian^ORCID,Zhen Mengyang^ORCID,Wang Yue^ORCID,Liu Zheng^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s12205-023-1327-y.pdf

Reference35 articles.

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2. Armaghani DJ (2018) Rock fragmentation prediction through a new hybrid model based on imperial competitive algorithm and neural network. Smart Construction Research 2(3), DOI: https://doi.org/10.18063/scr.v2i3.397

3. Aler J, Du Mouza J, Arnould M (1996) Evaluation of blast fragmentation efficiency and its prediction by multivariate analysis procedures. International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts 7(33):315A, DOI: https://doi.org/10.1016/0148-9062(95)00055-0

4. Cunningham CVB (1987) Fragmentation estimations and the Kuz-Ram model-four years on. Proc. 2nd Int. Symp. on Rock Fragmentation by Blasting 475–487

5. Castro-Garcia R, Suykens JAK (2016) Wiener system identification using best linear approximation within the LS-SVM framework. 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI), IEEE 1–6, DOI: https://doi.org/10.1109/LA-CCI.2016.7885698

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