An adaptive structural dominant failure modes searching method based on graph neural network

Abstract

Dominant failure modes (DFMs) of structural systems are integral to life prediction and reliability assessment. However, the computational efficiency of existing DFMs searching methods is constrained by neglecting the structural non-Euclidean properties. To break free from this shackle, this paper proposes a DFMs searching algorithm based on the graph neural network (GNN). The proposed algorithm can adaptively identify graph samples representing DFMs via completing graph classification. First, the target structural system is converted into an undirected graph to preserve its topological features. Second, a hierarchical graph attention mechanism is developed to establish the mapping relationship between structure intrinsic properties and DFMs. Finally, two adaptive sample selection strategies are devised to iteratively search DFMs and supplement graph datasets. In order to reduce the number of reliability analyses, the algorithm will terminate prematurely when unable to identify new DFMs. A 2D truss and a 3D frame are selected to test the computational efficiency and stability of the algorithm. The search results indicate that, despite providing different initial training sets, this GNN-based algorithm still converges to DFMs consistent with the result of Monte Carlo Simulation (MCS). Compared to the genetic algorithm (GA) and the β-unzipping method, the proposed algorithm exhibits higher computational efficiency.