Nonlinear analysis of reinforced concrete slabs under high-cyclic fatigue loading

• Main Authors: Zou, Chuanlong, Ibrahim, Zainah, Hashim, Huzaifa, Jamadin, Adiza, Ayough, Pouria
• Format: Article
• Published: Elsevier 2022
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.um.edu.my/40801/

Infrastructures are frequently vulnerable to sustained cyclic loads and structural vibration. The accumulated cyclic stresses will induce fatigue in the structures and contribute to their inadequate service lifespan. Consequently, analyzing the present structural health status by the structural stiffness measurement is crucial. This study investigated the fatigue performance and dynamic progressive damage behavior of reinforced concrete (RC) slabs under high-cyclic fatigue loadings using nonlinear finite element (FE) analysis. A new model was recommended for predicting the concrete's residual strength. The accuracy of the suggested model and the FE simulation was validated by comparing the predicted natural frequencies, mode shapes, residual strength, and crack characteristics of speci-mens with the experimental results. Finally, a novel model for determining the dynamic stiffness of RC slabs was developed. Results showed that the cumulative degradation of the natural frequency, stiffness, and damage development of steel rebars and concrete increased as the fatigue loading cycle increased, indicating that the dynamic response and fatigue damage for RC slabs in the later stages of high-cyclic fatigue loading were more severe. Additionally, the RC slab's natural frequency was reduced rapidly during the initial steps of fatigue and, after that, slowed noticeably. Validation of the dynamic stiffness model demonstrated its capability in predicting the stiffness of fatigued and damaged RC slabs. The results provide practical insights for analyzing the dynamic behavior of existing structures by considering the nonlinear progressive damage and may improve the effi-ciency of structural damage detection.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).