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Artificial intelligence is gaining increasing popularity in structural analysis. However, at the structural system level, the appropriateness of data representation, the paucity of data, and the physical interpretability of results are rarely studied and remain profound challenges. To fill such gaps, a physics‐informed model named StructGNN‐E (i.e., structural analysis based on graph neural network [GNN]–elastic) based on the GNN architecture, which is capable of implementing the elastic analysis of structural systems without labeled data, is proposed in this study. The systems with structural topologies and member configurations are organized as graph data and later processed by a modified graph isomorphism network. Moreover, to avoid dependence on big data, a novel physics‐informed paradigm is proposed to incorporate mechanics into deep learning (DL), ensuring the theoretical correctness of the results. Numerical experiments and ablation studies demonstrate the unique effectiveness of StructGNN‐E against common DL models, with an average accuracy of 99% and excellent computational efficiency. Due to its differentiability, StructGNN‐E is promising for bidirectionally linking structural parameters and analysis results, paving the way for a new end‐to‐end structural optimization method in the future. [ABSTRACT FROM AUTHOR]

Analyzes the approximate estimation formulae of the transient creep C integral and creep J integral for the elastic-power law creeping cracked body under constant displacement. Application of approximate analytic method; Concept of finite element method; Consideration of elastic-power law creeping material by the elastic strain rates.

Presents a study which evaluated the transverse strain of hyperelastic solids as a function of its longitudinal using a constitutive equation for compressible hyperelastic materials. Presentation of the equations; Strain energy function and stresses for hyperelastic materials; Application to compressible rubber-like materials under simple tension and/or compression conditions.

Focuses on study which investigated the characteristics of the elastic modulus of polymeric coatings, other bulk materials and the capabilities of the dynamic mechanical analyzer (DMA) used. Reason that the measurement of coating mechanical properties is important; Information on the DMA 7e which is controlled with the Pyris Series software; How the DMA indentations were compared with those from the Hystitron Tribscope, a nanoindentation instrument.