Author:
Jin Xiaoliang,Zhao Rui,Ma Yulin
Abstract
This paper presents a machine learning-based approach to estimating the compressive strength and elastic modulus of rocks. A hybrid model, GWO-ELM, was built based on an extreme learning machine network optimized by the grey wolf algorithm. The proposed model was carried out on 101 experimental datasets, and four commonly used models were used as benchmarks to evaluate the accuracy of the proposed model. The results showed that the proposed hybrid model can accurately achieve the prediction of elastic modulus and compressive strength with high correlation coefficients and small prediction errors. The prediction performance of the hybrid model is significantly better than the other four original models, and it is an alternative model for predicting the compressive strength and elastic modulus of rocks, which is recommended as an auxiliary tool for real-time prediction of rock mechanical properties.
Funder
Key Science and Technology Program of Henan Province
Subject
Geology,Geotechnical Engineering and Engineering Geology
Reference58 articles.
1. Multicriteria decision-making analysis based methodology for predicting carbonate rocks’ uniaxial compressive strength;Ersoy;Earth Sci. Res. J.,2012
2. Relationship between rock uniaxial compressive strength and digital core drilling parameters and its forecast method;Gao;Int. J. Coal Sci. Technol.,2021
3. Application of a novel constitutive model to evaluate the shear deformation of discontinuity;Xie;Eng. Geol.,2022
4. New constitutive model based on disturbed state concept for shear deformation of rock joints;Xie;Arch. Civ. Mech. Eng.,2022
5. Ying, J., Han, Z., Shen, L., and Li, W. (2020). Influence of Parent Concrete Properties on Compressive Strength and Chloride Diffusion Coefficient of Concrete with Strengthened Recycled Aggregates. Materials, 13.
Cited by
3 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