Development of A Hybrid Feature-Aided Volumetric Digital Image Correlation Method for Fine-Grained Soil Mixtures

Description

This proposed project will involve novel interdisciplinary research spanning the fields of fundamental geomechanics and computer vision to develop a hybrid feature-aided volumetric digital image correlation method to measure and compute the full-field strain distribution of fine-grained soil mixtures. A fine-grained soil mixture (FGSM) is a natural soil with varying proportions of clay, silt, and sand. The major thrust of this proposed project will be to extend and adapt a series of well-developed objectrecognition and image-matching techniques for feature detection, recognition, and matching of volumetric images acquired from in-situ X-ray microtomography scans of FGSMs subjected to external loading. The specific set of image matching techniques will include the three-dimensional (3D) speeded-up robust feature, 3D shape context, spherical harmonics-based particle morphological invariant and point-cloud registration. Depending on the soil compositional and microstructural characteristics, these imagematching techniques will be appropriately integrated to establish a powerful, efficient, and robust digital image correlation system that can be applied to any type of FGSM. This proposed project will generate important impacts in fundamental and applied research in a variety of fields, such as geotechnical engineering, construction materials, material science, computer vision and pattern recognition, concomitant with high economic and societal impacts. The research impacts will primarily derive from the newly established hybrid feature-aided digital image correlation tool, which will enable accurate, efficient, and robust computation of the full-field displacement and strain of FGSMs. Furthermore, advances in the knowledge frontiers of object recognition and image matching will lay the foundation for future interdisciplinary research in these areas. Economic and societal impacts will be achieved from the use of the newly developed digital image correlation tool in geotechnical engineering activities, such as site investigation, design, infrastructure construction and maintenance, or in the manufacturing, handling and processing of engineering materials in relevant industries.The results will contribute to the development of optimized engineering activities and processes, subsequently leading to improvements in geotechnical infrastructure, construction management, hazard warning and mitigation systems in Hong Kong and worldwide.