Structural Optimization and Fatigue Life Projection of Welded Truss Stacker Bridges for Mineral Stockpiling: An Integrated Approach Using Graph Neural Networks and Fractal Mechanics
DOI:
https://doi.org/10.65093/aci.v17.n1.2026.50Keywords:
graph neural network, fractal mechanics, structural fatigue, mobile bridgeAbstract
An integrated three-stage methodology is proposed for the predictive assessment and proactive maintenance of stacker bridges. A three-dimensional finite element analysis (FEA) is performed to characterize the displacement and von Mises stress fields, identifying critical nodes using a kinematic deformation scale factor of 908.2x. A fractal mapping of cracks in the critical plane is developed, estimating the fractal dimension Df of the crack trajectories using the box-counting method, which allows for the quantification of nonlinear propagation potential beyond linear elastic fracture mechanics (LEFM). Finally, a Graph Neural Network (GNN) is designed and trained that models the structural mesh as a graph G = (V, E, Φ), where the nodes represent the physical connections and the edges represent the structural beams, enabling the iterative prediction of nodal failure probabilities and the projection of remaining useful life (RUL). Results reveal a Structural Health Index (SHI) of 52.9%, identify node 253 as having the highest failure probability (57.7%), and project imminent systemic failure risk within a 3–6 month horizon. A 60-day intervention plan is formulated, including brace replacement, controlled 30-minute post-weld heat treatment (PWHT), and 100% liquid penetrant non-destructive examination (NDE), achieving a fatigue life improvement factor of 4x relative to the as-welded condition.
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