Digital Twin-Based Structural Assessment of Heavy Vehicle Suspension Components

In heavy-vehicle suspension system components, tie rod assemblies are subjected to buckling failure due to axial compressive loads. Euler-based formulations can provide valuable theoretical insight, but their direct applicability to real structural systems remains limited due to geometric imperfections, boundary-condition variability, and system-level compliance. In this study, an experimental work was performed on fifteen tie rod specimens with systematically varied geometries to investigate slenderness-dependent collapse behavior. Quasi-static axial compression tests were performed under pinned–pinned boundary conditions, and the maximum failure loads and corresponding displacements were extracted from the load–displacement responses. The experimental results revealed a pronounced deviation from classical Euler predictions and a strong dependence on geometric parameters. Three different stability regimes were identified via a slenderness-based collapse mapping and normalized load representation: high-slenderness (Euler-type), intermediate (Johnson-type), and low-slenderness (inelastic) fields. In this study, we proposed that the maps of collapse and normalized metrics can provide an experimental foundation for future geometry-aware predictive models. In this study, a complete digital twin system has not been implemented; however, the fundamental physical and data-driven building blocks required for regime-aware model updating and rapid stability classification for a digital twin system have been identified. In summary, this study presents an adaptable framework based on fundamental vehicle dynamics knowledge for the suspension and steering system components of heavy-duty vehicles for digital twin applications.