Steel reinforcement corrosion is becoming a critical issue due to the amount of old buildings still in operation with a high probability of substantial corrosion in their reinforcements. The strength and serviceability of corroded reinforced concrete (RC) slab is remarkably impacted by corrosion. Therefore, it is vital to understand more about the punching shear mechanism of slabs subjected to corrosion effects in order to make a good prediction regarding residual capacity. In this paper, critical shear crack theory (CSCT) was further developed to predict the residual punching shear strength of corroded RC slabs, considering the deterioration of material properties induced by corrosion. The effectiveness of modified CSCT was verified by comparison with the available experimental results. In addition, a validated finite element model that considers material deterioration and bond degradation was proposed to verify the accuracy of the proposed model. Finally, a parametric study of 240 cases was conducted to investigate the effect of some important factors on the punching shear behaviour of corroded RC slabs based on modified CSCT. The outcomes demonstrated that the residual punching shear capacity predicted by the proposed model is more accurate than existing design codes. The predicted failure modes also showed fairly good agreement with the experimental results.

Long-gauge fiber optic sensors have proven to be valuable tools for structural health monitoring, especially in reinforced concrete (RC) beam structures. While their application in this area has been well-documented, their use in RC columns remains relatively unexplored. This suggests a promising avenue for further research and development. This paper presents a thorough comparison of long-gauge fiber optic sensors and traditional measurement tools when used to monitor RC columns under small eccentric compressive loading. The evaluation focuses on the stability and precision of each sensor type. A monitoring system was developed for laboratory testing to assess the performance of various sensor types under specific conditions. The system incorporated four measurement schemes, utilizing a combination of embedded and surface-mounted long-gauge fiber optic sensors, linear variable differential transformers (LVDTs), and point sensors (strain gauges). Long-gauge fiber optic sensors, securely mounted on the concrete surface near the tensile side, were found to accurately measure both large and small deformations, outperforming LVDTs. Compared to strain gauges and embedded optic sensors, the long-gauge fiber optic sensors demonstrated superior average strain measurement and minimal interference from protective covers.

Slab–column structures are susceptible to sudden punching shear failure at connections due to the absence of traditional beam support, prompting the need for effective damage monitoring. This study employs an acoustic emission (AE) technique to investigate the failure process of reinforced concrete slab–column specimens, analyzing basic AE parameters (hits, amplitude, energy), improved b-value (Ib-value), and RA–AF correlation, while introducing a Gaussian Mixture Model (GMM) to establish a unified index integrating crack type identification and energy information. Experimental results show that AE parameters can effectively track different stages of crack development, with Ib-value reflecting the transition from micro-crack to macro-crack growth. The correlation between AE energy and structural strain energy enables quantitative damage assessment, while RA–AF analysis and GMM clustering reveal the shift from bending-dominated to shear-dominated failure modes. This study provides a comprehensive framework for real-time damage evaluation and failure mode prediction in slab–column structures, demonstrating that AE-based multi-parameter analysis and data-driven clustering methods can characterize damage evolution and improve the reliability of structural health monitoring.