Damage point prediction of a force chain based on the digital image correlation method

Area of Science:

• Physics
• Materials Science
• Engineering Mechanics

Background:

• Force chains significantly influence the stability of granular systems.
• Understanding force chain evolution is crucial for predicting material failure.
• Existing methods lack precise damage point prediction in granular systems.

Purpose of the Study:

• To introduce and verify a novel method for predicting damage points in granular force chains.
• To define displacement threshold (S_C) and angle threshold (θ_d) for damage prediction.
• To analyze the evolution of force chains in a granular system under load.

Main Methods:

• Utilized a two-dimensional steel ball particle system subjected to concentrated loading.
• Employed digital image correlation (DIC) to analyze particle displacement and movement direction.
• Continuously recorded deformation processes using a CCD camera.

Main Results:

• Successfully predicted damage points within the force chains of the steel ball system.
• Quantified particle displacement and movement directions during deformation.
• Validated the proposed displacement and angle thresholds for damage prediction.

Conclusions:

• The developed method accurately predicts damage points in granular force chains.
• The proposed thresholds (S_C and θ_d) are effective for stability analysis.
• This research offers a new approach to understanding and enhancing granular system stability.