Welkom...

dr. X. Yin (Xianfei)

Universitair docent

Expertises

Engineering & Materials Science
Defects
Inspection
Networks (Circuits)
Pipe
Prefabricated Construction
Productivity
Sewers
Television

Publicaties

Recent
Yuan, M., Li, Z., Li, X., Luo, X. , Yin, X., & Cai, J. (2021). Proposing a multifaceted model for adopting prefabricated construction technology in the construction industry. Engineering, Construction and Architectural Management. https://doi.org/10.1108/ECAM-07-2021-0613
Yin, X., Chen, Y., Bouferguene, A., Zaman, H., Al-Hussein, M., & Kurach, L. (2020). A deep learning-based framework for an automated defect detection system for sewer pipes. Automation in construction, 109, [102967]. https://doi.org/10.1016/j.autcon.2019.102967
Chen, Y., Bouferguene, A., Shen, Y. , Yin, X., & Al-Hussein, M. (2020). Bilevel Decision-Support Model for Bus-Route Optimization and Accessibility Improvement for Seniors. Journal of computing in civil engineering, 34(2), [04019057]. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000875
Yin, X., Liu, H., Chen, Y., Wang, Y., & Al-Hussein, M. (2020). A BIM-based framework for operation and maintenance of utility tunnels. Tunnelling and underground space technology, 97, [103252]. https://doi.org/10.1016/j.tust.2019.103252
Yin, X., Chen, Y., Bouferguene, A., & Al-Hussein, M. (2020). Data-driven bi-level sewer pipe deterioration model: Design and analysis. Automation in construction, 116, [103181]. https://doi.org/10.1016/j.autcon.2020.103181
Yin, X., Chen, Y., Bouferguene, A., Zaman, H., Al-Hussein, M., & Russell, R. (2020). Data-Driven Framework for Modeling Productivity of Closed-Circuit Television Recording Process for Sewer Pipes. Journal of construction engineering and management, 146(8), [04020093]. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001885
Yin, X., Bouferguene, A., & Al-Hussein, M. (2020). Data-Driven Sewer Pipe Data Random Generation and Validation. Journal of construction engineering and management, 146(12), [04020131]. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001937

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Verbonden aan Opleidingen

Bachelor

Master

Vakken Collegejaar  2021/2022

Vakken in het huidig collegejaar worden toegevoegd op het moment dat zij definitief zijn in het Osiris systeem. Daarom kan het zijn dat de lijst nog niet compleet is voor het gehele collegejaar.
 

Contactgegevens

Bezoekadres

Universiteit Twente
Faculty of Engineering Technology
Horst Complex (gebouwnr. 20), kamer Z208
De Horst 2
7522LW  Enschede

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Postadres

Universiteit Twente
Faculty of Engineering Technology
Horst Complex  Z208
Postbus 217
7500 AE Enschede