Dr Yue Hou

Uwch-ddarlithydd, Civil Engineering

yue.hou@abertawe.ac.uk

Swyddfa Academaidd - A_131
Llawr Cyntaf
Adeilad Canolog Peirianneg
Campws y Bae

Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig

Dolenni Ymchwil

Trosolwg

Mae Dr Yue Hou yn Uwch-ddarlithydd yn yr Adran Peirianneg Sifil yn y Gyfadran Gwyddoniaeth a Pheirianneg ym Mhrifysgol Abertawe, Y Deyrnas Unedig. Enillodd Dr Hou ei radd PhD mewn Peirianneg Sifil yn Virginia Tech, Unol Daleithiau America.

Mae Yue yn Gymrawd Sefydliad y Peirianwyr Sifil (FICE), yn Gymrawd y Sefydliad Ffiseg (FInstP), yn Gymrawd y Sefydliad Peirianneg a Thechnoleg (FIET), yn Uwch Aelod o’r IEEE, yn Aelod o’r ASCE, ac yn Beiriannydd Siartredig (CEng).

Mae Yue yn gwasanaethu fel Golygydd Cyswllt Traddodiadau IEEE Transactions on Intelligent Transportation Systems, Proceedings of the Institution of Civil Engineers (ICE) – ym meysydd Cynaliadwyedd Peirianneg, Cyfrifiaduron a Pheirianneg Drydanol ac mae’n aelod o fwrdd golygyddol Philosophical Transactions of the Royal Society A, The Baltic Journal of Road and Bridge Engineering a chyfnodolion rhyngwladol eraill.

Mae diddordebau ymchwil Yue yn canolbwyntio ar isadeiledd a deunyddiau trafnidiaeth gwydn a deallus, dysgu peirianyddol yn seiliedig ar ffiseg mewn peirianneg isadeiledd trafnidiaeth, dylunio isadeiledd a deunyddiau cynaliadwy gan ddefnyddio gefeilliaid digidol a chynaeafu ynni ar isadeiledd trafnidiaeth.

Meysydd Arbenigedd

Peirianneg Isadeiledd Trafnidiaeth
Peirianneg Sifil
Systemau Isadeiledd Trafnidiaeth Deallus
Deunyddiau Isadeiledd Trafnidiaeth

Cyhoeddiadau

Uchafbwyntiau Ymchwil

Zheng, Q., Hou, Y., Yang, H., Tan, P., Shi, H., Xu, Z., Ye, Z., Chen, N., Qu, X., Han, X., Zou, Y., Cui, X., Yao, H., Chen, Y., Yao, W., Zhang, J., Chen, Y., Liang, J., Gu, X...., & Wang, Z. (2022). Towards a sustainable monitoring: A self-powered smart transportation infrastructure skin. Nano Energy, 98, 107245
https://doi.org/10.1016/j.nanoen.2022.107245, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61796
Hou, Y., Shi, H., Chen, N., Liu, Z., Wei, H., & Han, Q. (2022). Vision Image Monitoring on Transportation Infrastructures: A Lightweight Transfer Learning Approach. IEEE Transactions on Intelligent Transportation Systems, 1-12.
https://doi.org/10.1109/tits.2022.3150536, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61802
Chen, N., Zhao, S., Gao, Z., Wang, D., Liu, P., Oeser, M., Hou, Y., & Wang, L. (2022). Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation. Construction and Building Materials, 323, 126580
https://doi.org/10.1016/j.conbuildmat.2022.126580, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61797
Hou, Y., Liu, S., Cao, D., Peng, B., Liu, Z., Sun, W., & Chen, N. (2022). A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22156-22165.
https://doi.org/10.1109/tits.2022.3160524, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61801
Hou, Y., Li, Q., Zhang, C., Lu, G., Ye, Z., Chen, Y., Wang, L., & Cao, D. (2021). The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis. Engineering, 7(6), 845-856.
https://doi.org/10.1016/j.eng.2020.07.030, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61799
Hou, Y., Zhao, S., Xue, Z., Liu, S., Song, B., Wang, D., Liu, P., Oeser, M., & Wang, L. (2022). Intelligent analysis of subbase strain based on a long-term comprehensive monitoring. Transportation Geotechnics, 33, 100720
https://doi.org/10.1016/j.trgeo.2022.100720, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61798
Hou, Y., Li, Q., Han, Q., Peng, B., Wang, L., Gu, X., & Wang, D. (2021). MobileCrack: Object Classification in Asphalt Pavements Using an Adaptive Lightweight Deep Learning. Journal of Transportation Engineering, Part B: Pavements, 147(1), 04020092
https://doi.org/10.1061/jpeodx.0000245, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61800
Liu, Z., Pan, S., Gao, Z., Chen, N., Li, F., Wang, L., & Hou, Y. (2023). Automatic intelligent recognition of pavement distresses with limited dataset using generative adversarial networks. Automation in Construction, 146, 104674
https://doi.org/10.1016/j.autcon.2022.104674, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62096
Yao, H., Zhao, S., Gao, Z., Xue, Z., Song, B., Li, F., Li, J., Liu, Y., Hou, Y., & Wang, L. (2023). Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study. Transportation Geotechnics, 40, 100957
https://doi.org/10.1016/j.trgeo.2023.100957, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62655
Yao, H., Xu, Z., Hou, Y., Dong, Q., Liu, P., Ye, Z., Pei, X., Oeser, M., Wang, L., & Wang, D. (2023). Advanced industrial informatics towards smart, safe and sustainable roads: A state of the art. Journal of Traffic and Transportation Engineering (English Edition), 10(2)
https://doi.org/10.1016/j.jtte.2023.02.001, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63058
http://dx.doi.org/10.1016/j.jtte.2023.02.001

Holl Ymchwil

Erthyglau Cylchgronau

Yao, H., Liu, Y., Lv, H., Huyan, J., You, Z., & Hou, Y. (2023). Encoder–decoder with pyramid region attention for pixel‐level pavement crack recognition. Computer-Aided Civil and Infrastructure Engineering, 0
https://doi.org/10.1111/mice.13128, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa65225
Pan, S., Yan, H., Liu, Z., Chen, N., Miao, Y., & Hou, Y. (2023). Automatic pavement texture recognition using lightweight few-shot learning. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2254)
https://doi.org/10.1098/rsta.2022.0166, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63895
http://dx.doi.org/10.1098/rsta.2022.0166
Hou, Y., Dong, Q., Wang, D., & Liu, J. (2023). Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 381(2254)
https://doi.org/10.1098/rsta.2022.0177, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63916
http://dx.doi.org/10.1098/rsta.2022.0177
Zhang, R., Li, F., Zhou, S., & Hou, Y. (2023). Carbon nanofiber dispersion in alkali solution and its reinforcement of alkali-activated volcanic ash-based geopolymers. Journal of Cleaner Production, 405, 137021
https://doi.org/10.1016/j.jclepro.2023.137021, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63067
http://dx.doi.org/10.1016/j.jclepro.2023.137021
Yao, H., Zhao, S., Gao, Z., Xue, Z., Song, B., Li, F., Li, J., Liu, Y., Hou, Y., & Wang, L. (2023). Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study. Transportation Geotechnics, 40, 100957
https://doi.org/10.1016/j.trgeo.2023.100957, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62655
Chen, N., Shi, H., Liu, R., Li, Y., Li, J., Xu, Z., Wang, D., Lu, G., Jing, B., & Hou, Y. (2023). A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects. IEEE Wireless Communications, 30(2), 76-81.
https://doi.org/10.1109/mwc.002.2200347, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63288
http://dx.doi.org/10.1109/mwc.002.2200347
Yao, H., Xu, Z., Hou, Y., Dong, Q., Liu, P., Ye, Z., Pei, X., Oeser, M., Wang, L., & Wang, D. (2023). Advanced industrial informatics towards smart, safe and sustainable roads: A state of the art. Journal of Traffic and Transportation Engineering (English Edition), 10(2)
https://doi.org/10.1016/j.jtte.2023.02.001, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa63058
http://dx.doi.org/10.1016/j.jtte.2023.02.001
Liu, Z., Pan, S., Gao, Z., Chen, N., Li, F., Wang, L., & Hou, Y. (2023). Automatic intelligent recognition of pavement distresses with limited dataset using generative adversarial networks. Automation in Construction, 146, 104674
https://doi.org/10.1016/j.autcon.2022.104674, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa62096
Hou, Y., Liu, S., Cao, D., Peng, B., Liu, Z., Sun, W., & Chen, N. (2022). A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22156-22165.
https://doi.org/10.1109/tits.2022.3160524, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61801
Zheng, Q., Hou, Y., Yang, H., Tan, P., Shi, H., Xu, Z., Ye, Z., Chen, N., Qu, X., Han, X., Zou, Y., Cui, X., Yao, H., Chen, Y., Yao, W., Zhang, J., Chen, Y., Liang, J., Gu, X...., & Wang, Z. (2022). Towards a sustainable monitoring: A self-powered smart transportation infrastructure skin. Nano Energy, 98, 107245
https://doi.org/10.1016/j.nanoen.2022.107245, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61796
Chen, N., Zhao, S., Gao, Z., Wang, D., Liu, P., Oeser, M., Hou, Y., & Wang, L. (2022). Virtual mix design: Prediction of compressive strength of concrete with industrial wastes using deep data augmentation. Construction and Building Materials, 323, 126580
https://doi.org/10.1016/j.conbuildmat.2022.126580, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61797
Hou, Y., Zhao, S., Xue, Z., Liu, S., Song, B., Wang, D., Liu, P., Oeser, M., & Wang, L. (2022). Intelligent analysis of subbase strain based on a long-term comprehensive monitoring. Transportation Geotechnics, 33, 100720
https://doi.org/10.1016/j.trgeo.2022.100720, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61798
Hou, Y., Shi, H., Chen, N., Liu, Z., Wei, H., & Han, Q. (2022). Vision Image Monitoring on Transportation Infrastructures: A Lightweight Transfer Learning Approach. IEEE Transactions on Intelligent Transportation Systems, 1-12.
https://doi.org/10.1109/tits.2022.3150536, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61802
Hou, Y., Li, Q., Zhang, C., Lu, G., Ye, Z., Chen, Y., Wang, L., & Cao, D. (2021). The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis. Engineering, 7(6), 845-856.
https://doi.org/10.1016/j.eng.2020.07.030, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61799
Hou, Y., Li, Q., Han, Q., Peng, B., Wang, L., Gu, X., & Wang, D. (2021). MobileCrack: Object Classification in Asphalt Pavements Using an Adaptive Lightweight Deep Learning. Journal of Transportation Engineering, Part B: Pavements, 147(1), 04020092
https://doi.org/10.1061/jpeodx.0000245, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa61800
Cai, X., Tang, X., Pan, S., Wang, Y., Yan, H., Ren, Y., Chen, N., & Hou, Y. (n.d.) Intelligent recognition of defects in high‐speed railway slab track with limited dataset. Computer-Aided Civil and Infrastructure Engineering
https://doi.org/10.1111/mice.13109, SU Repository: https://cronfa.swan.ac.uk/Record/cronfa64680
http://dx.doi.org/10.1111/mice.13109

Modiwlau a Addysgir

EG-295 Introduction to Transportation Engineering

Transportation engineering plays the fundamental role for the operation of society. This module will deliver basic knowledge, basic governing equations, and computation skills in transportation engineering. Typical transportation modes, including road transportation and railway transportation, will be introduced in detail. The whole life cycle of road and railways, from design, construction, operation, to maintenance will also be introduced.
EGEM00 Transportation Engineering

This module will deliver advanced knowledge, governing equations, and computation skills in transportation engineering. The tradition design, construction, evaluation and maintenance of major transportation infrastructures, including roads and railways, will be introduced. The improvement and rationality on the sustainable design, construction, evaluation and maintenance of the transportation infrastructures to mitigate the climate emergency will also be introduced. The state-of-the-art machine learning-, material characterisation-, and advanced sensing- relevant research in transportation engineering will be introduced at master level.