Luo, T. X., Zheng, Q., & Hou, F. (2026). Unsupervised deep-learning model for quantitative diagnosis of tunnel lining cavity condition using interfering GPR data. Tunnelling and Underground Space Technology Incorporating Trenchless Technology Research, 170. https://doi.org/10.1016/j.tust.2025.107373
ISO-690 (author-date, English)LUO, Tess Xianghuan, ZHENG, Qingzhou and HOU, Feifei, 2026. Unsupervised deep-learning model for quantitative diagnosis of tunnel lining cavity condition using interfering GPR data. Tunnelling and Underground Space Technology incorporating Trenchless Technology Research. 1 April 2026. Vol. 170, , . DOI 10.1016/j.tust.2025.107373.
Modern Language Association 9th editionLuo, T. X., Q. Zheng, and F. Hou. “Unsupervised Deep-Learning Model for Quantitative Diagnosis of Tunnel Lining Cavity Condition Using Interfering GPR Data”. Tunnelling and Underground Space Technology Incorporating Trenchless Technology Research, vol. 170, Apr. 2026, https://doi.org/10.1016/j.tust.2025.107373.
Mohr Siebeck - Recht (Deutsch - Österreich)Luo, Tess Xianghuan/Zheng, Qingzhou/Hou, Feifei: Unsupervised deep-learning model for quantitative diagnosis of tunnel lining cavity condition using interfering GPR data, Tunnelling and Underground Space Technology incorporating Trenchless Technology Research 2026,
Emerald - HarvardLuo, T.X., Zheng, Q. and Hou, F. (2026), “Unsupervised deep-learning model for quantitative diagnosis of tunnel lining cavity condition using interfering GPR data”, Tunnelling and Underground Space Technology Incorporating Trenchless Technology Research, Vol. 170, available at:https://doi.org/10.1016/j.tust.2025.107373.