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Fangyu Liu

Die folgende Bibliografie enthält alle in dieser Datenbank indizierten Veröffentlichungen, die mit diesem Namen als Autor, Herausgeber oder anderweitig Beitragenden verbunden sind.

  1. Liu, Fangyu / Xu, Yongjia / Li, Junlin / Wang, Linbing (2025): Graph Neural Network–Based Spatiotemporal Structural Response Modeling in Buildings. In: Journal of Computing in Civil Engineering, v. 39, n. 2 (März 2025).

    https://doi.org/10.1061/jccee5.cpeng-6229

  2. Yang, Jingjing / Wang, Zhiyuan / Li, Guangchen / Wu, Chengjin / Liu, Fangyu / Chen, Yixing (2025): AutoBPS-carbon: A tool to estimate multi-year dynamic embodied and operational carbon emissions in the building sector. In: Journal of Building Engineering, v. 101 ( 2025).

    https://doi.org/10.1016/j.jobe.2025.111942

  3. Liu, Jian / Liu, Fangyu / Wang, Linbing (2024): Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization. In: Journal of Traffic and Transportation Engineering (English Edition), v. 11, n. 3 (Juni 2024).

    https://doi.org/10.1016/j.jtte.2023.10.002

  4. Liu, Fangyu / Liu, Jian / Wang, Linbing / Al-Qadi, Imad L. (2024): Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning. In: Automation in Construction, v. 161 (Mai 2024).

    https://doi.org/10.1016/j.autcon.2024.105355

  5. Liu, Fangyu / Ding, Wenqi / Qiao, Yafei / Wang, Linbing (2024): Transfer learning-based encoder-decoder model with visual explanations for infrastructure crack segmentation: New open database and comprehensive evaluation. In: Underground Space, v. 17 (August 2024).

    https://doi.org/10.1016/j.undsp.2023.09.012

  6. Liu, Jian / Liu, Fangyu / Wang, Zhen / Fanijo, Ebenezer O. / Wang, Linbing (2023): Involving prediction of dynamic modulus in asphalt mix design with machine learning and mechanical-empirical analysis. In: Construction and Building Materials, v. 407 (Dezember 2023).

    https://doi.org/10.1016/j.conbuildmat.2023.133610

  7. Chang, Jun / Zhang, Hong / Liu, Fangyu / Cui, Kai (2024): Exploring the mechanism of micro-nano bubble water in enhancing the mechanical properties of sulfoaluminate cement-based materials. In: Construction and Building Materials, v. 411 (Januar 2024).

    https://doi.org/10.1016/j.conbuildmat.2023.134400

  8. Liu, Fangyu / Li, Junlin / Wang, Linbing (2023): PI-LSTM: Physics-informed long short-term memory network for structural response modeling. In: Engineering Structures, v. 292 (Oktober 2023).

    https://doi.org/10.1016/j.engstruct.2023.116500

  9. Liu, Fangyu / Liu, Jian / Wang, Linbing (2022): Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning. In: Automation in Construction, v. 143 (November 2022).

    https://doi.org/10.1016/j.autcon.2022.104575

  10. Liu, Jian / Liu, Fangyu / Zheng, Chuanfeng / Fanijo, Ebenezer O. / Wang, Linbing (2022): Improving asphalt mix design considering international roughness index of asphalt pavement predicted using autoencoders and machine learning. In: Construction and Building Materials, v. 360 (Dezember 2022).

    https://doi.org/10.1016/j.conbuildmat.2022.129439

  11. Liu, Jian / Liu, Fangyu / Zheng, Chuanfeng / Zhou, Daodao / Wang, Linbing (2022): Optimizing asphalt mix design through predicting the rut depth of asphalt pavement using machine learning. In: Construction and Building Materials, v. 356 (November 2022).

    https://doi.org/10.1016/j.conbuildmat.2022.129211

  12. Liu, Jian / Liu, Fangyu / Gong, Hongren / Fanijo, Ebenezer O. / Wang, Linbing (2022): Improving asphalt mix design by predicting alligator cracking and longitudinal cracking based on machine learning and dimensionality reduction techniques. In: Construction and Building Materials, v. 354 (November 2022).

    https://doi.org/10.1016/j.conbuildmat.2022.129162

  13. Liu, Fangyu / Wang, Linbing (2022): UNet-based model for crack detection integrating visual explanations. In: Construction and Building Materials, v. 322 (März 2022).

    https://doi.org/10.1016/j.conbuildmat.2021.126265

  14. Liu, Fangyu / Liu, Jian / Wang, Linbing (2022): Deep learning and infrared thermography for asphalt pavement crack severity classification. In: Automation in Construction, v. 140 (August 2022).

    https://doi.org/10.1016/j.autcon.2022.104383

  15. Liu, Fangyu / Ye, Zhoujing / Wang, Linbing (2022): Deep transfer learning-based vehicle classification by asphalt pavement vibration. In: Construction and Building Materials, v. 342 (August 2022).

    https://doi.org/10.1016/j.conbuildmat.2022.127997

  16. Liu, Jian / Liu, Fangyu / Zheng, Chuanfeng / Zhou, Daodao / Wang, Linbing (2022): Optimizing asphalt mix design through predicting effective asphalt content and absorbed asphalt content using machine learning. In: Construction and Building Materials, v. 325 (März 2022).

    https://doi.org/10.1016/j.conbuildmat.2022.126607

  17. Liu, Fangyu / Ding, Wenqi / Qiao, Yafei / Wang, Linbing (2020): An artificial neural network model on tensile behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag power. In: Frontiers of Structural and Civil Engineering, v. 14, n. 6 (August 2020).

    https://doi.org/10.1007/s11709-020-0712-6

  18. Liu, Fangyu / Ding, Wenqi / Qiao, Yafei (2020): Experimental investigation on the tensile behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder. In: Construction and Building Materials, v. 241 (April 2020).

    https://doi.org/10.1016/j.conbuildmat.2020.118000

  19. Liu, Fangyu / Ding, Wenqi / Qiao, Yafei (2019): Experimental investigation on the flexural behavior of hybrid steel-PVA fiber reinforced concrete containing fly ash and slag powder. In: Construction and Building Materials, v. 228 (Dezember 2019).

    https://doi.org/10.1016/j.conbuildmat.2019.116706

  20. Liu, Fangyu / Ding, Wenqi / Qiao, Yafei (2019): An experimental investigation on the integral waterproofing capacity of polypropylene fiber concrete with fly ash and slag powder. In: Construction and Building Materials, v. 212 (Juli 2019).

    https://doi.org/10.1016/j.conbuildmat.2019.04.027

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