The emergence of cementitious materials with post-cracking strain hardening stress strain response in tension presents opportunities for retrofitting reinforced concrete elements through the application of very thin jackets, a technique that preserves the original geometry of the component. This retrofitting solution is studied experimentally in this work by replacing the damaged cover of lightly reinforced structural elements, which were previously tested under cyclic displacement reversals, simulating earthquake effects. Test specimens were detailed to represent older construction practices, where inadequate lap splicing of longitudinal reinforcement, light transverse reinforcement and thin concrete covers were common. Upon cyclic loading of the retrofitted components, the contribution of the thin jackets to the strength and deformation recovery of the old type reinforced concrete elements was examined considering the pre-existing damage. It was found that the efficacy of cover replacement with strain-hardening composites is significant not only for strength recovery but also in terms of enhanced deformability of the retrofitted component. The experimental response envelope was simulated using advanced nonlinear finite element modeling to gain improved insights regarding the stress state in the cover-replacement retrofitting layer, and to explore the performance of this retrofitting method for parameter values beyond the range of the experimental program. The amount of confinement exerted by the strain hardening cover was related to the tensile strength of the cover material and it controlled the strength recovery. However, by suppressing all brittle modes of failure along the shear span of the component, the reinforcement anchorage in the footing dominated the eventual failure mode.

The assessment methods for estimating the behavior of the complex mechanics of reinforced concrete (RC) structural elements were primarily based on experimental investigation, followed by collective evaluation of experimental databases from the available literature. There is still a lot of uncertainty in relation to the strength and deformability criteria that have been derived from tests due to the differences in the experimental test setups of the individual research studies that are being fed into the databases used to derive predictive models. This research work focuses on structural elements that exhibit pronounced strength degradation with plastic deformation and brittle failure characteristics. The study’s focus is on evaluating existing models that predict the shear strength of RC columns, which take into account important factors including the structural element’s ductility and axial load, as well as the contributions of specific resistance mechanisms like that of concrete, transverse, and longitudinal reinforcement. Significantly improved predictive models are proposed herein through the implementation of machine learning (ML) algorithms on refined datasets. Three ML models, LINREG, POLYREG-HYT, and XGBoost-HYT-CV, were used to develop different predictive models that were able to compute the shear strength of RC columns. According to the numerical findings, POLYREG-HYT and XGBoost-HYT-CV derived models outperformed other ML models in predicting the shear strength of rectangular RC columns with the correlation coefficient having a value R greater than 99% and minimal errors. It was also found that the newly proposed predictive model derived a 2-fold improvement in terms of the correlation coefficient compared to best available equation in the international literature.

The assessment methods for estimating the behavior of the complex mechanics of reinforced concrete (RC) structural elements were primarily based on experimental investigation, followed by the collective evaluation of experimental databases from the available literature. There is still a lot of uncertainty in relation to the strength and deformability criteria that have been derived from tests due to the differences in the experimental test setups of the individual research studies that are being fed into the databases used to derive predictive models. This research work focuses on structural elements that exhibit pronounced strength degradation with plastic deformation and brittle failure characteristics. The study’s focus is on evaluating existing models that predict the shear strength of RC columns, which take into account important factors including the structural element’s ductility and axial load, as well as the contributions of specific resistance mechanisms like that of concrete, transverse, and longitudinal reinforcement. Significantly improved predictive models are proposed herein through the implementation of machine learning (ML) algorithms on refined datasets. Three ML models, LREGR, POLYREG-HYT, and XGBoost-HYT-CV, were used to develop different predictive models that were able to compute the shear strength of RC columns. According to the numerical findings, POLYREG-HYT- and XGBoost-HYT-CV-derived models outperformed other ML models in predicting the shear strength of rectangular RC columns with the correlation coefficient having a value R greater than 99% and minimal errors. It was also found that the newly proposed predictive model derived a 2-fold improvement in terms of the correlation coefficient compared to the best available equation in international literature.

This paper explores methods of simulating the behaviour of building structures under progressive collapse conditions through alternative models of different levels of structural idealization. Such models have been applied in many previous studies, but there is insufficient information regarding their reliability and their ability to represent actual structural behaviour as the level of idealization is reduced. To address this, the study adopts the alternative load path method through the well-established concept of notional column removal, performed via nonlinear static analyses of models with different levels of structural idealization. The focus is on the interaction between the directly affected structural members and the surrounding structure, which is shown to significantly influence the overall response under progressive collapse. The results demonstrate that this interaction depends on multiple factors and cannot be reliably captured when the surrounding structure is not explicitly modelled. Building on this finding, the study systematically evaluates how reduced models can be enhanced to better represent these interactions and proposes strategies for defining boundary conditions that preserve global structural behaviour. Overall, the study advances understanding of model idealization effects and provides practical guidance for developing efficient reduced models for progressive collapse simulations without compromising essential aspects of structural response.

Corrosion-induced deterioration of reinforced concrete (RC) structures remains a major concern in structural engineering, especially in coastal or chloride-rich environments. Finding quick, long lasting and cost-effective solutions is a key point of modern civil engineering. In the study presented herein we investigated the structural performance of corroded RC elements strengthened with the commercially available innovative retrofitting materials such as Strain-Hardening Cementitious Composites (SHCCs) and Fiber Reinforced Polymers (FRPs). RC specimens are subjected to accelerated corrosion via the impressed current technique, then retrofitted with SHCC overlays. Flexural tests were used to evaluate the retrofits in terms of load-bearing capacity, ductility, and failure mechanisms. Image-based deformation measurements complement these tests, providing data for the validation of advanced nonlinear finite element models developed in ABAQUS. Numerical evaluation supported the experimental results. Additionally, a second remediating methodology Fiber-Reinforced Polymers (FRPs) was evaluated through FEA. Retrofitting corroded beams with SHCC markedly improved strength and ductility, restoring and exceeding uncorroded capacity by ∼55% in finite element analysis (FEA) whereas FRP retrofitting increased capacity by ∼67.6% compared to corroded beams, offering higher peak strength than SHCC but with more brittle post-peak behavior. SHCC proved highly effective for restoring strength and deformability, while FRP offered similar strength gains with less ductility. The experimental evaluation of FRP subjected specimens and the final comparison of the two methods is ongoing.

This paper explores methods of simulating the behaviour of building structures under progressive collapse conditions through alternative models of different levels of structural idealization. Such alternative simulation models have been applied in many previous studies of the problem, but there is insufficient information regarding the degree of reliability and appropriate representation of the actual structural behaviour as the model is simplified by reducing the level of structural idealization. To investigate the problem, the study adopts the alternative load path method through the well-established concept of notional column removal. The study focuses on the interaction of the directly affected structural members with their neighboring structural components. The results demonstrate the significant influence of this parameter on the overall structural response under progressive collapse conditions. Most importantly, it is demonstrated that this parameter depends on many factors and is difficult to quantify without explicitly analyzing the behaviour of the surrounding structure. As a conclusion, a local failure that can potentially lead to progressive collapse should be analyzed in the context of the global behaviour of the structure. Alternatively, effective methods for simulating the boundary conditions of individual structural members should be studied and developed to enable the application of reduced analysis models.