Accelerating Buckling Load Factor Prediction in Timber Frame Walls Using an Encoder–Decoder Surrogate Model

In structural engineering, the iterative optimisation of complex timber wall components is fundamentally limited by the prohibitive computational costs of non-linear Finite Element Analysis (FEA). To overcome this bottleneck, this study introduces a highly efficient machine learning-based surrogate model utilising a convolutional encoder–decoder architecture to predict the global buckling load factor (BLF) directly from structural topology. We evaluate three distinct modeling strategies: a Direct prediction model, a Sequential model, and a multitask Dual-Loss model designed to simultaneously predict the BLF and reconstruct spatial tensile stress fields. Experimental results demonstrate that both the Direct and Dual-Loss approaches achieve near-perfect predictive accuracy on in-distribution data, yielding a coefficient of determination (R2) of approximately 0.996. Crucially, these surrogates accelerate inference times by a factor of roughly 12,800 compared to traditional iterative solvers, reducing evaluation times from hours to mere seconds. Furthermore, the models exhibit exceptional robustness and extrapolative capability under rigorous out-of-distribution testing. The models maintain high predictive fidelity when subjected to cross-dataset distributional shifts (R2 ≈0.94) and when evaluating intentionally low-performing, highly vulnerable configurations (R2≈0.967). Extensive validation on structurally disjoint, hold-out wall geometries confirms the models’ ability to generalise to entirely unseen topologies without introducing systematic bias (R2>0.95). By successfully internalising the underlying physical principles of load redistribution, this surrogate framework provides a reliable, computationally inexpensive foundation for enabling real-time, autonomous generative design and structural optimisation.