Article: Nonlinear stress-strain prediction method for pipeline steel based on multi-scale adaptive network Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2026 Vol.30 No.6 pp.108 - 128 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

Title: Nonlinear stress-strain prediction method for pipeline steel based on multi-scale adaptive network

Authors: Ning Dong; Suxiang Zhang

Addresses: College of Information, Wuhan Vocational College of Software and Engineering (Wuhan Open University), Wuhan, 430205, Hubei, China ' College of Electronic Science and Information Engineering, Science and Technology College of Hubei University of Arts and Science, No. 28 East Yinji Street, Xiangcheng District, Xiangyang, 441025, Hubei, China

Abstract: 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.

Keywords: nonlinear deformation prediction; multi-scale feature extraction; pipeline steel modelling.

DOI: 10.1504/IJDMB.2026.153893

International Journal of Data Mining and Bioinformatics, 2026 Vol.30 No.6, pp.108 - 128

Received: 17 Dec 2025
Accepted: 09 Mar 2026
Published online: 29 May 2026 *

Title: Nonlinear stress-strain prediction method for pipeline steel based on multi-scale adaptive network

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