Detection of Structural Vulnerabilities in Multi-Cavity Steel Plate Shear Walls Using Improved Deep Neural Networks

Steel Plate Shear Walls (SPSWs) are a significant structural system because they can dissipate energy and have a very high lateral stiffness. However, the discovery and elimination of vital structural vulnerabilities, mainly in multi-cavity configurations, is still a major challenge. This study utilizes developments in the deep learning era to improve the identification and representation of such vulnerabilities. An improved DNN architecture was employed to analyze the effectiveness of multi-cavity SPSWs under different loading conditions. The proposed method combines hybrid information extraction techniques with various geometries and materials to ensure a reliable prediction of structural element failures. The tests have shown highly positive results, with the enhanced DNN outperforming conventional procedures by achieving higher accuracy, lower false-positive rates, and superior generalization across various test cases. This work demonstrates a new way to detect weaknesses in a structure, thereby developing an effective tool for engineers to prevent the sustainability and safety of SPSWs in critical infrastructure.