Integrating Grain to Ground Motion Data for Critical Rock Bridge and Slope Failure Analysis

Rock masses can fail catastrophically with little warning, yet their stability depends on hidden internal connections that channel forces and localise damage. Among these, rock bridges–a “known unknown” in slope mechanics–lock fractured rock and control how failure unfolds, but their concealed geometry and uncertain strength obscure where and how damage initiates. We present a data‐driven framework that treats rock masses as force–motion flow networks, with three key innovations: the critical rock bridge path is conceptualised as a recurrent bottleneck within the prefailure force‐transmission network of a rock mass, damage evolution cast as a network attack process, and internal connections interpreted as kinematic constraints resisting relative motion. By linking grain‐scale micromechanics with radar‐derived surface displacements on active slopes, we track the evolution of kinematic constraints and quantify prefailure damage. This physics‐informed, data‐driven approach integrates laboratory and field scales, providing a framework for identifying and characterising damage propagation along critical rock bridges in the prefailure regime across scales, on Earth and beyond.