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Related Experiment Video

Updated: Sep 17, 2025

Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method

Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method

Published on: January 16, 2019

A Macro-meso damage coupling rock mass damage model based on improved internal crack analysis.

Haian Liang¹, Miao He¹, Hongliang Zhao¹

¹School of Civil and Architectural Engineering, East China University of Technology, Nanchang, Jiangxi, China.

|June 30, 2025

Summary

A new rock damage constitutive model integrates macroscopic and microscopic damage, accurately predicting jointed rock failure. This model enhances understanding of rock mechanics and material properties under stress.

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Area of Science:

Geotechnical Engineering
Rock Mechanics
Materials Science

Background:

Geological factors create rock defects like joints, degrading mechanical properties.
Understanding jointed rock deformation and failure is crucial for engineering applications.

Purpose of the Study:

To develop a rock damage constitutive model incorporating geometric and mechanical joint properties.
To combine macroscopic and microscopic damage analysis for a comprehensive model.

Main Methods:

Utilized damage mechanics principles and the Lemaitre strain equivalence hypothesis.
Developed a constitutive model accounting for both macro and micro-scale rock damage.
Validated the model using uniaxial compression tests on jointed clay-like rock.

Main Results:

The model accurately reflects rock failure processes and aligns with experimental data.
Model parameters m and F₀ quantify jointed rock brittleness and average strength, respectively.
The combined analysis approach is effective in representing rock mechanical behavior.

Conclusions:

The proposed damage constitutive model provides clear physical significance for jointed rocks.
The model successfully captures the mechanical behavior and failure mechanisms of rocks.
Experimental validation confirms the model's accuracy and applicability.