This paper proposed an attention-based multi-scale deformation prediction network (AMSD-Net) for nonlinear mechanical response modelling.Using multi-dimensional physical parameters of pipeline steels as inputs, AMSD-Net integrates a hierarchical feature extraction backbone composed of Inception modules, squeeze-and-excitation (SE) channel attention, and convolutional block attention module (CBAM) spatial attention to capture deformation characteristics at different spatial scales.Parallel multi-scale convolutional pathways and a dual-attention mechanism are employed to recalibrate channel-wise and spatial features in a data-driven manner.Experimental evaluations on simulation datasets generated from X70 and X90 pipeline steels show that AMSD-Net achieves lower root mean square error and mean absolute error in stress, strain, and deformation prediction compared with representative baseline models, while maintaining stable fitting behaviour across the elastic-plastic transition region.AMSD-Net outperforms conventional baselines in predicting nonlinear deformation and failure strength, enabling more efficient and accurate data-driven pipeline integrity assessment.
Dong et al. (Thu,) studied this question.