High-fidelity mesoscopic heterogeneity modeling and mechanical properties investigation of rock based on mask R-CNN and DDA
GUO Longxiao1, WANG Yan1, SHAO Wenhui1, CHEN Guangqi1,2, MA Guowei1
(1. School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin 300401, China;
2. Department of Civil and Structural Engineering, Kyushu University, Fukuoka, 819–0395, Japan)
Abstract：The macroscopic mechanical behavior of rock is fundamentally governed by its mesostructure. However, most existing mesoscopic DDA models rely on ideal geometric units and structural simplifications, making it challenging to accurately represent the actual mesoscopic characteristics of rock. To address this limitation, this study proposes a mesoscopic numerical modeling approach for rock that integrates Mask R-CNN and DDA, enabling the direct construction of high-fidelity numerical models from real mesoscopic images. The proposed method first enhances mineral boundary features in rock images through wavelet transform, and then utilizes Mask R-CNN for accurate identification and segmentation of minerals and pores. Based on this, a high-precision DDA model is established to systematically analyze the influence of mineral composition and pore characteristics on the mechanical properties of rock. The results indicate that quartz can inhibit the initiation and propagation of microcracks due to its high elastic modulus and strong interface contact strength, while the low contact strength of feldspar exacerbates rock stress concentration, leading to a linear positive correlation between the quartz-to-feldspar ratio (QFR) and uniaxial compressive strength (UCS). The geometric heterogeneity of pores and minerals induces local stress concentration in the rock, promoting an increase in microcracks in high-porosity areas and resulting in a nonlinear negative correlation between porosity and UCS. This method overcomes the limitations of traditional modeling associated with oversimplification, achieving a realistic reproduction of rock mesostructures and highlighting the impact of mesoscopic heterogeneity on macroscopic mechanical behavior. It provides a novel tool and insights for revealing the mesoscopic mechanisms underlying the macroscopic mechanical behavior of rock.
郭龙骁1，王 燕1，邵文慧1，陈光齐1，2，马国伟1. 基于Mask R-CNN与DDA的岩石细观非均质性高保真建模及力学特性研究[J]. 岩石力学与工程学报, 2026, 45(4): 1217-1227.
GUO Longxiao1, WANG Yan1, SHAO Wenhui1, CHEN Guangqi1,2, MA Guowei1. High-fidelity mesoscopic heterogeneity modeling and mechanical properties investigation of rock based on mask R-CNN and DDA. , 2026, 45(4): 1217-1227.
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2026, Vol. 45 
