【论文】

[1] Zhao, Q., Iwama, K., Dai, J.G., Zhang, D., Zhao, X., Xue, H., Maekawa, C., Zhao, X.L.* In-Service Performance Deterioration Analysis of FRP Reinforced-Concrete Structures Exposed to Natural Marine Environments: A Theoretical Approach and Application. ASCE’s Journal of Composites for Construction. 2026, 30(1), 04025058.
[2] Zhao, X., Zhang, D., Zhao, Q.*, Zhang, P.F., Tuerxunmaimaiti, Y. Effects of fiber types on interlaminar shear strength degradation of epoxy-based G/CFRP bars under a marine concrete pore solution environment. ASCE’s Journal of Materials in Civil Engineering. 2026, 38(5): 04026093
[3] Zhao, Q., Zhang, D., Liu, J., Iwama, K., Zhang, P. F., Zeng, L., & Zhao, X.L.* (2025). Generalized Degradation Model and Bond Failure Analysis of Pultruded Basalt/Carbon/Glass FRP Bars and Profiles in Concrete Environments. Advances in Structural Engineering, 2026, 29(3) 442–459.
[4] Zhao, X., Zhang, P. F., Zhao, Q.*, Zhang, D., Tuerxunmaimaiti, Y., & Cao, H. A SHAP algorithm-based prediction of the interlaminar shear strength degradation of G/BFRP bars embedded in concrete exposed to marine environment. Case Studies in Construction Materials, 2025, 22, e04770.
[5] Tuerxunmaimaiti, Y., Zhao, X.L., Zhang, D.*, Zhao, Q.*, Zhang, P.F., Zhao, X., Iqbal, M. Predicting fatigue slip and fatigue life of FRP rebar-concrete bonds using tree-based and theory-informed learning models. International Journal of Fatigue, 2025, 193, 108816.
[6] Zhao, Q., Zhao, X.L., Zhang, D.*, & Duan, L. P. Effects of exposure in seawater sea-sand concrete pore solution on fatigue performance of carbon FRP bars. Composites Science and Technology, 2024, 247，110418.
[7] Zhao, Q., Zhao, X.L., Zhang, D.*, Dai, J.G., & Xue, X.Y. Degradation of GFRP bars with epoxy and vinyl ester matrices in a marine concrete environment: An Experimental study and theoretical modelling. ASCE’s Journal of Composites for Construction, 2024, 28(2): 04024004.
[8] Zhao, Q., Iwama, K., Dai, J.G., Liu, J., Zhang, D., Maekawa, C., Zhao, X.L.* Deterioration modelling of GFRP-reinforced cement-based concrete infrastructure in service under the natural inland atmospheric environment. Construction and Building Materials, 2024, 447, 138005.
[9] Zhao, Q.*, Iwama, K., Zhao, X.L. Durability evaluation of maritime concrete served in ocean environment. Proceedings of the Fourth World Conference on Floating Solutions: WCFS 2024, In World Conference on Floating Solutions (pp. 635-641). Singapore: Springer Nature Singapore.
[10] 赵齐, 张大旭*, 赵晓林, & 王文华. (2022). 环氧基GFRP筋在海水海砂混凝土孔溶液环境下的损伤演化试验与模型研究.土木工程学报, 2022, 55(9): 27-45.
[11] Zhao, Q., Zhang, D.*, Zhao, X.L, & Sharma, S. Modelling damage evolution of carbon fiber-reinforced epoxy polymer composites in seawater sea sand concrete environment. Composite Science and Technology, 2021, 215, 108961.
[12] Zhao, Q., Zhao, J.*, Dang, J.T., Chen, J.W., & Shen, F.Q. Experimental investigation of shear walls using carbon fiber reinforced polymer bars under cyclic lateral loading. Engineering Structures, 2019, 191: 82-91.
[13] Zhao, X.L., Zhao, Q.*, Iwama, K., Dai, J.G., Kai, M.F. Degradation prediction of FRP bars within concrete bridge decks in service. Bridge Maintenance, Safety, Management, Digitalization and Sustainability – Jensen, Frangopol & Schmidt (eds), 1st Edition, CRC Press. 491-496. https://doi.org/10.1201/9781003483755-54
[14] Zhang, P. F., Zhang, D.*, Zhao, Q., Zhao, X., & Tuerxunmaimaiti, Y. A semantic-aware transfer learning framework via natural language processing to predict the cross-material bond strength of composite bars with limited data. Engineering Applications of Artificial Intelligence, 2025, 160, 112011.
[15] Zhao, X., Zhang, P. F., Zhang, D.*, Zhao, Q., & Tuerxunmaimaiti, Y. Prediction of interlaminar shear strength retention of FRP bars in marine concrete environments using XGBoost model. Journal of Building Engineering, 2025, 105, 112466.
[16] Zhang, P.F., Zhang, D.*, Zhao, X.L., Zhao, X., Iqbal, M., Tuerxunmaimaiti, Y., Zhao, Q. Natural language processing-based deep transfer learning model across diverse tabular datasets for bond strength prediction of composite bars in concrete. Computer-Aided Civil and Infrastructure Engineering. 2024. https://doi.org/10.1111/mice.13357
[17] Du, Y.L., Zhang, D.*, Zhang, Y., Guo, W.Y., Zhao, Q. Predicting tensile behaviour of plain weave CMCs using a nonlinear data-driven constitutive model for fibre tow composites. Ceramics International, 2024, 9(50): 16142-16154.
[18] Du, Y., Zhang, D.*, Guo, W., Chen, C., Wang, L., Chen, M., ... & Zhao, Q. A nonlinear data-driven constitutive model for fibre tows to predict in-plane shear behaviour of plain weave CMCs. Composite Structures, 2025, 365, 119181.
[19] Zhang, P. F., Zhang, D., Zhao, X., Tuerxunmaimaiti, Y., Zhao, Q., & Iqbal, M. A data-physics fusion framework integrating XGBoost and PINN in data-limited conditions for prediction of FRP-UHPC beam test bond strength. Composite Structures, 2025, 376, 119848.
[20] Zhang, P.F., Zhao, X.L., Zhang, D.*, Iqbal, M., Zhao, X., Zhao, Q., Tuerxunmaimaiti, Y., Yu, C. Prediction of bond strength and failure mode of FRP bars embedded in UHPC or UHPSSC utilising extreme gradient boosting technique. Composite Structures, 2024, 346, 118437.