FEM-based analysis of SPCS shear walls: effects of concrete compaction imperfections

Steel Plate Composite Shear (SPCS) walls are critical for seismic-resistant structures due to their superior energy dissipation and load-bearing capabilities. To address the increasing demand for smart infrastructure, this study develops an advanced framework integrating Optical Coherence Tomography (OCT), LiDAR, and Finite Element Method (FEM) simulations to evaluate the effects of concrete compaction imperfections on structural performance. By leveraging high-resolution optical sensing technologies, OCT and LiDAR provide detailed surface and subsurface defect data, which are incorporated into a multi-scale FEM model to simulate damage evolution and stress distribution under cyclic seismic loads. Furthermore, a Convolutional Neural Network (CNN) is designed to automate defect classification and severity prediction, employing cross-modal feature fusion to enhance diagnostic precision. The proposed diagnostic framework is validated through field experiments using a UAV-LiDAR system, achieving a defect detection accuracy of over 89%. Results indicate that critical imperfections (≥8 mm) significantly impair energy dissipation, highlighting the importance of precise defect detection and modeling in smart infrastructure systems. This study demonstrates the utility of combining optical sensing, FEM simulations, and AI-driven diagnostics to provide an intelligent, non-destructive evaluation system for SPCS shear walls, paving the way for the development of future smart building technologies.