Trans-l,4-polyisoprene (TPI) shape memory polymer composites with different chopped carbon fiber mass fractions were prepared to study the effects of different chopped carbon fiber mass fractions and temperatures on the TPI shape memory polymer composites in this paper. While guaranteeing the shape memory effect of TPI shape memory polymers, the carbon fiber fillers also significantly enhanced the mechanical properties of the polymers. The thermodynamic properties and shape memory properties of TPI shape memory polymers were studied by a differential scanning calorimeter (DSC) test, dynamic mechanical analysis (DMA) test, thermal conductivity test, static tensile test, mechanical cycle test, thermodynamic cycling test and shape memory test. Furthermore, the tensile fracture interface of TPI shape memory polymer composites was analyzed by scanning electron microscopy. The experimental results show that when the chopped carbon mass fraction fiber is 8%, TPI shape memory polymers have good shape memory properties and the best mechanical properties.

Shape memory epoxy polymer (SMEP) composite specimens with different graphene oxide (GO) contents were manufactured to study the effects of GO mass fractions on epoxy polymer composites. While ensuring the shape memory effect of SMEP, the addition of GO also remarkably strengthened the mechanical performance of the polymers. Analyses of the epoxy polymer composites’ thermal, mechanical, and shape memory performance were conducted through carrying out dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA), and static tensile, three-point bending, impact, and shape memory tests. Moreover, the tensile fracture, bending fracture, and impact fracture interfaces of epoxy resin composites were examined with scanning electron microscopy. The final test results indicated that when the GO content was 0.8 wt %, SMEP composites had good shape memory performance and optimum thermal and mechanical performance.

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.

Through studying the effects of the Al–5Ti-x(Mg–30%Ce) refiner on the microstructure of the Al–7Si alloy, we investigated the hardness, tensile, and impact properties of the Al–Si alloy before and after refinement. The refinement results show that Mg–30%Ce can improve the refinement effect of Al–5Ti on Al–7Si, and also refine α-Al and eutectic Si in Al–7Si. When 2%Mg–30%Ce is added, the refining effect of the aluminum–silicon alloy is most obvious. After refinement, the hardness and tensile properties of the Al–7Si alloy improve. When 2%Mg–30%Ce is added to the refiner, the tensile properties of the Al–Si alloy are the best. Dynamic mechanical tests were conducted on refined Al–7Si alloy. The impact toughness of Al–7Si–(Al–5Ti-2(Mg–30%Ce) improved and the impact toughness reached 34.91 J/cm², which is 78.7% higher than that of Al–7Si–(Al–5Ti) specimens. The separate Hopkinson pressure bar test results show that, under a high strain rate, the strain rate sensitivity of the prepared Al–Si alloy is significant. The specimens of Al–7Si–(Al–5Ti-2(Mg–30%Ce) demonstrated the best anti-impact performance at the same velocity.

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.