A novel ensemble machine learning model to predict mine blasting–induced rock fragmentation-Reference-Cited by-同舟云学术

A novel ensemble machine learning model to predict mine blasting–induced rock fragmentation

Published:2023-04-26 Issue:5 Volume:82 Page:
ISSN:1435-9529
Container-title:Bulletin of Engineering Geology and the Environment
language:en
Short-container-title:Bull Eng Geol Environ

Author:

Yari Mojtaba,He Biao,Armaghani Danial Jahed,Abbasi Payam,Mohamad Edy Tonnizam

Publisher

Springer Science and Business Media LLC

Subject

Geology,Geotechnical Engineering and Engineering Geology

Link

https://link.springer.com/content/pdf/10.1007/s10064-023-03138-y.pdf

Reference67 articles.

1. Abdelghany WK, Hammed MS, Radwan AE (2022) Implications of machine learning on geomechanical characterization and sand management: a case study from Hilal field, Gulf of Suez, Egypt. J Pet Explor Prod Technol 1–16

2. Armaghani DJ, Hajihassani M, Mohamad ET et al (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7:5383–5396

3. Armaghani DJ, Koopialipoor M, Bahri M et al (2020) A SVR-GWO technique to minimize flyrock distance resulting from blasting. Bull Eng Geol Environ. https://doi.org/10.1007/s10064-020-01834-7

4. Armaghani DJ, Mahdiyar A, Hasanipanah M et al (2016) Risk assessment and prediction of flyrock distance by combined multipleregression analysis and Monte Carlo simulation of quarry blasting. Rock Mech Rock Eng 49:1–11. https://doi.org/10.1007/s00603-016-1015-z

5. Azimi Y (2006) Investigation of seismic wave due to blasting in Sungun copper mine. Amirkabir University of Technology (Tehran Polytechnic), MSc Iran

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Mean Block Size Prediction in Rock Blast Fragmentation Using TPE-Tree-Based Model Approach with SHapley Additive exPlanations;Mining, Metallurgy & Exploration;2024-08-08

2. Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms;Applied Sciences;2024-06-27

3. Extension of reliability information of Z-numbers and fuzzy cognitive map: Development of causality-weighted rock engineering system to predict and risk assessment of blast-induced rock size distribution;International Journal of Rock Mechanics and Mining Sciences;2024-06

4. A comprehensive survey on machine learning applications for drilling and blasting in surface mining;Machine Learning with Applications;2024-03

5. State-of-the-art review of machine learning and optimization algorithms applications in environmental effects of blasting;Artificial Intelligence Review;2024-01