A self-correcting strategy for the digital volume correlation displacement field based on image matching: application to poor speckle quality and complex large deformation

Abstract Digital volume correlation (DVC) is widely used for the analysis of three-dimensional displacement and strain fields based on computed tomography (CT) scans. However, the applicability of DVC methods is limited in regard to geomaterials: the speckles are directly correlated with the microstructure of the material, and the speckle structure cannot be artificially altered, resulting in generally poor speckle quality. Additionally, most geomaterials exhibit elastoplastic properties and undergo complex large deformations under external loading, sometimes leading to strain localization phenomena. These factors contribute to inaccuracies in the displacement field obtained through DVC, and at present, there is a shortage of correction methods and accuracy assessment techniques for the displacement field. If the accuracy of the DVC displacement field is sufficiently high, the gray residue of the two volume images before and after deformation should be minimal, and the use of this characteristic to develop a correction method for the displacement field is feasible. The proposed self-correcting strategy of the displacement field is based on image matching for local DVC method, which is based on the experimental measurement error. We demonstrated the effectiveness of the proposed method by performing CT triaxial tests on granite residual soil. Without adding other DVC parameters, the gray residue showed that the proposed method can effectively improve the accuracy of the displacement field. Additionally, the accuracy evaluation method can reasonably estimate the accuracy of the displacement field. The proposed method can effectively improve the accuracy of the DVC three-dimensional displacement field for the state of speckles with poor quality and complex large deformation.