Understanding the interface shear behavior between clay and structures is crucial in geotechnical engineering. The mechanism of the roughness effect in the shear process between the clay and structures was studied to reveal the macroscopic and microscopic interface shear behavior. The different surface protrusion shapes of the structures were produced using a three‐dimensional (3D) printer. Direct shear tests were conducted to analyze the shear failure modes and peak and residual strengths under different conditions. Subsequently, a discrete element method (DEM) numerical analysis was employed to study the contact network, soil fabric evolution, shear zone, coordination number, and void ratio variations in the interface shear. The test results indicated that the shear interfaces exhibited the same failure mode under various conditions, and the peak and residual strengths showed a strong positive correlation with roughness. The results obtained from numerical calculations match the experimental findings. The contact orientations and principal stresses shifted during the shear process, and the shear zone, coordination number, and void ratio also showed regular changes with the change of roughness. The evolution of microscopic parameters in DEM can effectively help explain the macroscopic interface shear behavior.

This study employs a PID (Proportion, Integral, Differential)-based search algorithm (PSA) to achieve structural damage identification (SDI), localization, and quantification. We developed finite element programs for a 10-element simply supported beam, a 21-element truss, and a 7-story steel frame, assigning damage factors to each element as design variables. The Relative Frequency Change Rate (RFCR) and Modal Assurance Criterion (MAC) were calculated as objective functions for PSA iteration; comparative studies were then conducted against Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) in terms of damage identification accuracy, computational efficiency, and noise robustness. Results demonstrate that PSA achieves exceptional damage localization accuracy within 1% error in severity under noise-free conditions. With 1–3% noise, PSA maintains precise damage localization despite minor severity estimation errors, while other algorithms exhibit false positives in intact elements. Within the fixed number of iterations, PSA outperforms GA and PSO in computational efficiency. Although SA shows faster computation, it significantly compromises identification accuracy and fails in damage detection. The regularization term enables PSA to maintain noise-resistant damage identification even in a 70-element frame structure, demonstrating its potential for robust damage assessment across diverse structural types, scales, and noisy environments.