Strength estimation of granular rocks using a microstructure-based empirical model. (December 2022)

• Record Type: Journal Article
• Authors: Liu, Guang; Cai, Ming; Yao, Huayan; Rong, Guan; Zhang, Zhenhua; Wang, Yanqiao
• Is Part Of: Engineering failure analysis. Volume 142(2022)
• Publication Date: 2022-12
• Subjects: Strength estimation -- Rock failure -- Microstructure -- Heterogeneity; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106761
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: A heterogeneity index was used to measure the heterogeneity degrees of rock grain structure. The relation between rock strength and microstructure characteristics was studied. A microstructure-based empirical model was proposed to estimate the rock strength during failure. The proposed strength model was validated by experimental and simulation data. Abstract: A natural rock is an assembly of various mineral grains, and the microscopic grain structure of the rock influences its strength. The strength of granular rocks can be related to microscopic grain structure; however, empirical models linking microscopic grain structure and rock strength are rarely reported. This research presents a characterization method for geometric heterogeneity induced by the grain size difference. The heterogeneity of the microscopic scanning images of a coarse-grained marble is analyzed using a proposed heterogeneity index. A microstructure-based empirical model is proposed to describe the relation between the compressive strength of rock and its micro-property parameters. This microstructure-based empirical model is verified using numerical and experimental test data. The analysis results show that the proposed model can predict the strength of a granular rock better than existing models of similar nature. The model parameters for different rocks such as LdB granite, Aminadav dolomite, and Bohemian Massif granite are obtained by nonlinear regression analysis. This model is also employed to predict crack initiation and damage stresses of rocks, showing results in good agreement with the experimental data.