Shape memory alloy (SMA), a type of smart material, is widely used in the design of reinforcement and repair, isolation, and shock absorption of building structures because of its outstanding characteristics, such as the shape memory effect (SME), superelasticity (SE), and high damping. It not only improves the bearing capacity, ductility, and mechanical properties of the structural components of buildings but can also effectively slow down the strong response of engineering structures under the effect of an earthquake. It plays a key role in energy dissipation and shock absorption as well as sustainable development. To promote the application of SMA in building structures, this paper summarizes the research on the use of SMA as a reinforcing material in building structures, including work related to SMA material characteristics and types, SMA-reinforced structural components, and SMA isolation devices. In addition, the shortcomings of SMA applications in building structures are analyzed, and valuable suggestions for future research methods are put forward. SMA has been applied to engineering practice in the form of embedded and external reinforcement, which shows that it has broad application prospects in future buildings.

To address the existing challenges of lacking a unified and reliable shear capacity prediction model for fiber-reinforced polymer (FRP)-strengthened reinforced concrete beams (FRP-SRCB) and the excessive experimental workload, this study establishes a shear capacity prediction model for FRP-SRCB based on machine learning (ML). First, the correlation between input and output parameters was analyzed by the Pearson correlation coefficient method. Then, representative single model (ANN) and integrated model (XGBoost) algorithms were selected to predict the dataset, and their performance was evaluated based on three commonly used regression evaluation metrics. Finally, the prediction accuracy of the ML model was further verified by comparing it with the domestic and foreign design codes. The results manifest that the shear capacity exhibits a strong positive correlation with the beam width and effective height. Compared to the ANN model, the XGBoost-based prediction model achieves determination coefficients (R²) of 0.999 and 0.879 for the training and test sets, respectively, indicating superior predictive accuracy. Furthermore, the shear capacity calculations from design codes show significant variability, demonstrating the superior predictive capability of ML algorithms. These findings offer a guideline for the design and implementation of FRP reinforcement in actual bridge engineering.

The shape memory effect induced by thermally exciting shape memory alloy (SMA) provides an active constraint method for structural reinforcement. To investigate the axial compression performance and failure mechanism of SMA strips confined concrete columns, axial compression tests and real-time acoustic emission (AE) monitoring were performed on concrete columns with diverse pre-strain levels and constraint methods. The results reveal that the constraint of SMA strips improves the mechanical properties and inhibits the brittle failure. Based on the correlation between AE characteristic parameters and stress–time curves, the internal failure of confined specimens is classified into three stages: micro-crack initiation, crack stable development and macroscopic crack formation. The rise angle value grows and average frequency value reduces as the damage progresses, manifesting that the shear crack is in the stable propagation stage. The b-value generally diminishes as the load rises, illustrating that the cracking level inside the specimen is continuously increasing. Moreover, compared with the PC40-50-40 specimen, the AC40-50-40 specimen generates highly active AE signals. The distribution of AE damage events indicates that active constraint significantly accelerates the initiation and propagation of cracks in concrete columns during compression loading.

The prestressed shape memory alloy (SMA) strips confined columns are a novel reinforcement method, which not only exerts active confinement stress on the core concrete but also avoids the common stress hysteresis problem in reinforcement. This paper performed axial compression tests on eight sets of concrete columns with varying SMA strip width, net spacing, and pre-strain, and the impacts of these variables regarding the failure pattern, bearing capacity, and deformability of the specimens were investigated. A calculation model for the bearing capacity of SMA strips actively confined to concrete columns was established and contrasted with the prediction performance of the BP neural network. The results indicate that compared to the unconfined column, SMA strip-confined columns exhibit obvious ductile failure under compression, with the highest increase of bearing capacity and deformability reaching up to 20.27% and 24.96%, respectively. The confinement effect becomes better and better with the increasing strip width or the decreasing strip net spacing. When the strip pre-strain gradually increases, the bearing capacity of confined columns gradually improves, while the deformability first enhances and then weakens. The experimental data of other scholars is used to verify that the calculation results accord with the experimental results well, and the prediction precision of the proposed calculation model exceeds that of the BP neural network. Meanwhile, it is confirmed that the BP neural network exhibits a high fitting level in bearing capacity prediction (R²_training=0.990 and R²_test=0.965), offering a novel approach for predicting the bearing capacity of structures.

A hypersonic flow field over a blunt wedge with or without roughness is simulated by a direct numerical simulation method. The effect of isolated and distributed roughnesses on the steady and unsteady hypersonic flow field and boundary layer is analyzed. The shape of roughness is controlled by cubic polynomial. The evolution of disturbance waves caused by slow acoustic wave in the boundary layer is investigated by fast Fourier spectrum analysis. The results show that there is a great influence of roughness on the evolution of disturbance waves in the hypersonic boundary layer. The disturbance waves are promoted in the upstream-half region of roughness while suppressed in the downstream-half region of roughness. There is always a mode competition among different modes both in the temporal domain and in the frequency domain in the boundary layer, and mode competition is affected by roughness. The location of the dominant mode which is changed to a second-order harmonic mode from the fundamental mode moves upstream. The vortices caused by roughness also impact the evolution of disturbance waves in the boundary layer. The fundamental mode is suppressed in the vortex region while other harmonic modes are promoted.