Development of empirical models to predict stope wall stability in open stope mines using gene-expression programming-Reference-Cited by-同舟云学术

Development of empirical models to predict stope wall stability in open stope mines using gene-expression programming

Published:2023-10-25 Issue:11 Volume:16 Page:
ISSN:1866-7511
Container-title:Arabian Journal of Geosciences
language:en
Short-container-title:Arab J Geosci

Author:

Amirkiyaei Vahid^ORCID,Ghasemi Ebrahim,Kadkhodaei Mohammad Hossein

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s12517-023-11730-5.pdf

Reference44 articles.

1. Adoko AC, Vallejos J, Trueman R (2020) Stability assessment of underground mine stopes subjected to stress relaxation. Min Technol Trans Inst Min Metall 129:30–39. https://doi.org/10.1080/25726668.2020.1721995

2. Adoko AC, Saadaari F, Mireku-Gyimah D, Imashev A (2022) A feasibility study on the implementation of neural network classifiers for open stope design. Geotech Geol Eng 40:677–696. https://doi.org/10.1007/S10706-021-01915-8

3. Ahmad A, Chaiyasarn K, Farooq F et al (2021) Compressive strength prediction via gene expression programming (GEP) and artificial neural network (ANN) for concrete containing RCA. Build 11(11):324. https://doi.org/10.3390/BUILDINGS11080324

4. Capes G (2009) Open stope hangingwall design based on general and detailed data collection in unfavourable hangingwall conditions. University of Saskatchewan

5. Deng X, Liu Q, Deng Y, Mahadevan S (2016) An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci (Ny) 340–341:250–261. https://doi.org/10.1016/J.INS.2016.01.033

Cited by 2 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. Refined Approaches for Open Stope Stability Analysis in Mining Environments: Hybrid SVM Model with Multi-optimization Strategies and GP Technique;Rock Mechanics and Rock Engineering;2024-07-11

2. Classification of coal bursting liability of some chinese coals using machine learning methods;Scientific Reports;2024-06-18