Aug 2026· International journal for numerical and analytical methods in geomechanics (Print)· 0 citations· 37 references
Abstract
Calibration of microparameters in numerical simulations is a critical factor affecting model accuracy. To determine the relationship between macro‐ and microparameters in composite rock masses and the influence of soft rock layer proportions on mechanical properties, this study employed PFC2D software to construct numerical models of composite rock masses with varying soft‐to‐hard layer thickness ratios. A systematic analysis was conducted to investigate the influence of microparameters on macro‐mechanical properties. For composite rock bodies with varying soft rock layer thicknesses, as the soft rock proportion increases, the failure mode gradually shifts from shear failure dominated by hard rock to foliated failure dominated by soft rock. Cracks propagate along bedding planes and are constrained by hard rock layers. When the soft rock layer thickness increases from 10% to 90%, the total number of cracks increases by approximately twofold, while the proportion of shear cracks decreases from 75% to 40%. The crack counting rate exhibits exponential growth with stress and synchronizes with stress amplitude, peaking at 180–200 times/s
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. Laboratory test results align with numerical simulations, demonstrating that the PFC model accurately predicts composite rock mass strength (error ≤3.7%) and elastic modulus (error ≤4.6%). This study provides theoretical support for correlating macro‐ and micro‐mechanical properties in composite rock masses of varying hardness, offering significant reference value for stability assessment and reinforcement design in underground engineering.
High-stress soft rock roadways in deep underground engineering often exhibit significant time-dependent deformation due to strong rheological behavior of surrounding rock. To investigate the deformation characteristics and support effect, a composite viscoelastic constitutive model considering anchored and unanchored r...
Yong-Sheng Han, Shulin Lu, K. Guo et al.· Applied Sciences· 0 citations
To mitigate the violent movement of overlying strata in goaf areas, rock–concrete composite support systems are widely utilized. However, the mechanical behavior of such systems under the influence of complex pre-existing defects, such as arc-shaped fractures, remains insufficiently understood. This study aims to c...
Shu-Bin Zhang, Hong-Kai Zhao, B. Hong et al.· International Journal of Geo...· 0 citations
Rock-filled concrete (RFC) is a three-phase heterogeneous composite composed of large-size rockfill, self-compacting concrete (SCC), and the interfacial transition zone (ITZ) between them. Its mechanical performance is jointly affected by the rockfill skeleton effect and the properties of the ITZ. Existing studies stil...
Qian Li, Ning Liu, Xiaocong Tang et al.· PLoS ONE· 0 citations
The periodic fluctuation of reservoir water levels induces dry‐wet cycles, deteriorating sandy rock slope stability and potentially triggering collapses. This study investigates the degradation mechanism of Three Gorges Reservoir fine sandstone through uniaxial compression tests and PFC2D simulations under varying dry‐...
Senlin Gao, Qing-Yang Ren, Bin Chen et al.· International journal for nu...· 0 citations
Water significantly modifies rock mechanical performance and fracture characteristics through water content and water distribution. Nevertheless, the evolution laws of rock mechanical properties and underlying fracture mechanisms under variable water conditions remain incompletely clarified. In this study, uniaxial com...
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
Writing as a participant and researcher, PhD student JS Tan SM ’22 has co-authored a new book about the rise of tech worker protests and the employer backlash that followed.
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