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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. Liang, Jia / Zhang, Qipeng / Gu, Xingyu (2024): Small-sample data-driven lightweight convolutional neural network for asphalt pavement defect identification. In: Case Studies in Construction Materials, v. 21 (December 2024).

    https://doi.org/10.1016/j.cscm.2024.e03643

  2. Liang, Jia / Gu, Xingyu / Jiang, Dong / Zhang, Qipeng (2024): CNN-based network with multi-scale context feature and attention mechanism for automatic pavement crack segmentation. In: Automation in Construction, v. 164 (August 2024).

    https://doi.org/10.1016/j.autcon.2024.105482

  3. Gu, Xingyu / Xu, Xiaoyu / Zhang, Qipeng / Sun, Lijun / Zhou, Zhou (2024): Study on the correlation between spatial variability of asphalt mixture material parameters and fracture performance. In: Case Studies in Construction Materials, v. 20 (July 2024).

    https://doi.org/10.1016/j.cscm.2024.e02989

  4. Zhang, Qipeng / Gu, Xingyu / Dong, Qiao / Liang, Jia (2023): Modified fractional-Zener model—Numerical application in modeling the behavior of asphalt mixtures. In: Construction and Building Materials, v. 388 (July 2023).

    https://doi.org/10.1016/j.conbuildmat.2023.131690

  5. Liang, Jia / Zhang, Qipeng / Gu, Xingyu (2024): Lightweight convolutional neural network driven by small data for asphalt pavement crack segmentation. In: Automation in Construction, v. 158 (February 2024).

    https://doi.org/10.1016/j.autcon.2023.105214

  6. Zhang, Qipeng / Gu, Xingyu / Liang, Jia / Yu, Zilu / Dong, Qiao / Jiang, Jiwang (2023): Application of a stochastic damage model to predict the variability of creep behavior for asphalt mixtures. In: Case Studies in Construction Materials, v. 18 (July 2023).

    https://doi.org/10.1016/j.cscm.2023.e02078

  7. Zhang, Qipeng / Gu, Xingyu / Li, Song / Ding, Jitong / Liang, Jia (2022): Experimental and analytical methods for evaluating the high temperature viscoelastic properties of fine aggregate matrix. In: Materials and Structures, v. 55, n. 7 (5 August 2022).

    https://doi.org/10.1617/s11527-022-01900-4

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