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The following bibliography contains all publications indexed in this database that are linked with this name as either author, editor or any other kind of contributor.

  1. Ye, Jianlong / Yu, Hongchuan / Liu, Gaoyang / Zhou, Jiong / Shu, Jiangpeng (2024): Component Identification and Depth Estimation for Structural Images Based on Multi-Scale Task Interaction Network. In: Buildings, v. 14, n. 4 (27 March 2024).

    https://doi.org/10.3390/buildings14040983

  2. Shu, Jiangpeng / Yu, Hongchuan / Liu, Gaoyang / Yang, Han / Chen, Yanjuan / Duan, Yuanfeng (2023): BO-Stacking: A novel shear strength prediction model of RC beams with stirrups based on Bayesian Optimization and model stacking. In: Structures, v. 58 (December 2023).

    https://doi.org/10.1016/j.istruc.2023.105593

  3. Liu, Gaoyang / Ding, Wei / Shu, Jiangpeng / Strauss, Alfred / Duan, Yuanfeng (2023): Two-Stream Boundary-Aware Neural Network for Concrete Crack Segmentation and Quantification. In: Structural Control and Health Monitoring, v. 2023 (February 2023).

    https://doi.org/10.1155/2023/3301106

  4. Sun, Bochao / Cui, Wenjun / Liu, Gaoyang / Zhou, Biao / Zhao, Weijian (2023): A hybrid strategy of AutoML and SHAP for automated and explainable concrete strength prediction. In: Case Studies in Construction Materials, v. 19 (December 2023).

    https://doi.org/10.1016/j.cscm.2023.e02405

  5. Sun, Bochao / Li, Peichen / Wang, Dianchao / Ye, Jun / Liu, Gaoyang / Zhao, Weijian (2023): Evaluation of mechanical properties and anisotropy of 3D printed concrete at different temperatures. In: Structures, v. 51 (May 2023).

    https://doi.org/10.1016/j.istruc.2023.03.045

  6. Liu, Gaoyang / Sun, Bochao (2023): Concrete compressive strength prediction using an explainable boosting machine model. In: Case Studies in Construction Materials, v. 18 (July 2023).

    https://doi.org/10.1016/j.cscm.2023.e01845

  7. Yang, Yuanzhang / Liu, Gaoyang (2023): Data-Driven Shear Strength Prediction of FRP-Reinforced Concrete Beams without Stirrups Based on Machine Learning Methods. In: Buildings, v. 13, n. 2 (14 February 2023).

    https://doi.org/10.3390/buildings13020313

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