Dr. Jitao Zhao

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Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

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29 May 2024 in Materials

Journal Article

The Utilization of Shape Memory Alloy as a Reinforcing Material in Building Structures: A Review

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.

14 February 2019 in Metals

Journal Article

Effect of a New Grain Refiner (Al–Ti–Mg–Ce) on Hardness, Tensile, and Impact Properties of Al–7Si Alloy

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.

1 June 2025 in Buildings

Journal Article

Prediction of Shear Capacity of Fiber-Reinforced Polymer-Reinforced Concrete Beams Based on Machine Learning

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.

About Me

Dr. Jitao Zhao has a position as an Associate Professor and is currently working at the School of Civil and Architecture Engineering, Panzhihua University, Panzhihua, China. She holds a bachelor’s degree and a master’s degree in Engineering Mechanics from the School of Mechanics and Engineering of Liaoning University of Engineering and Technology, Fuxin, China. She also holds a PhD in Solid Mechanics from the School of Aerospace and Architectural Engineering of Harbin Engineering University, Harbin, China. She was a lecturer at the School of Science at Heilongjiang University of Science and Technology, Harbin, China. Her research directions are the mechanical properties of metal materials and the comprehensive utilization of industrial solid waste. She lectures on 10 undergraduate and postgraduate courses such as “Elastic-Plastic Mechanics”, edits and publishes one “Twelfth Five-Year Plan” textbook, and participates in the compilation of one case textbook. She won second prize in the National Coal Industry Education and Teaching Achievement Award, first prize in the school-level teaching achievement award, three school-level teaching awards, a third-class municipal science and technology award, and a first-class provincial university science and technology award. She presided over two teaching research and education reform projects. She has two authorized invention patents and four authorized utility model patents.

Dr. Jitao Zhao

Dr. Jitao Zhao

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About Me