Superelastic Shape Memory Alloys (SE SMAs) are unique alloys that have the ability to undergo large deformations and return to their undeformed shape by removal of stresses. This study aims at assessing the seismic behavior of beam-column joints reinforced with SE SMAs. Two large-scale beam-column joints were tested under reversed cyclic loading. While the first joint was reinforced with regular steel rebars, SE SMA rebars were used in the second one. Both joints were selected from a Reinforced Concrete (RC) building located in the high seismic region of western Canada and designed and detailed according to current Canadian standards. The behavior of the two specimens under reversed cyclic loading, including their drifts, rotations, and ability to dissipate energy, were compared. The results showed that the SMA-reinforced beam-column joint specimen was able to recover most of its post-yield deformation. Thus, it would require a minimum amount of repair even after a strong earthquake.

There have been abundant experimental studies exploring ultra-high-performance concrete (UHPC) in recent years. However, the relationships between the engineering properties of UHPC and its mixture composition are highly nonlinear and difficult to delineate using traditional statistical methods. There is a need for robust and advanced methods that can streamline the diverse pertinent experimental data available to create predictive tools with superior accuracy and provide insight into its nonlinear materials science aspects. Machine learning is a powerful tool that can unravel underlying patterns in complex data. Accordingly, this study endeavors to employ state-of-the-art machine learning techniques to predict the compressive strength of UHPC using a comprehensive experimental database retrieved from the open literature consisting of 810 test observations and 15 input features. A novel approach based on tabular generative adversarial networks was used to generate 6513 plausible synthetic data for training robust machine learning models, including random forest, extra trees, and gradient boosting regression. While the models were trained using the synthetic data, their ability to generalize their predictions was tested on the 810 experimental data thus far unknown and never presented to the models. The results indicate that the developed models achieved outstanding predictive performance. Parametric studies using the models were able to provide insight into the strength development mechanisms of UHPC and the significance of the various influential parameters.