366
Views
1
CrossRef citations to date
0
Altmetric
Articles

A hybrid physical data informed DNN in axial displacement prediction of immersed tunnel joint

, , &
Pages 169-180 | Received 09 Jun 2022, Accepted 15 Jan 2023, Published online: 12 Feb 2023

References

  • Bauduin, C., and A. A. Kirstein. 2022. “Design, Construction and Monitoring of an Underwater Retaining Wall Close to an Existing Immersed Tunnel.” Tunnelling and Underground Space Technology 120, doi:10.1016/j.tust.2021.104311.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45: 5–32. doi:10.1023/A:1010933404324.
  • Buster, G., M. Bannister, A. Habte, D. Hettinger, G. Maclaurin, M. Rossol, M. Sengupta, and Y. Xie. 2022. “Physics-guided Machine Learning for Improved Accuracy of the National Solar Radiation Database.” Solar Energy 232: 483–492. doi:10.1016/j.solener.2022.01.004.
  • Chen, T., and C. Guestrin. 2016. “XGBoost: A Scalable Tree Boosting System.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
  • Chen, J., and Y. Liu. 2021. “Probabilistic Physics-Guided Machine Learning for Fatigue Data Analysis.” Expert Systems with Applications 168. doi:10.1016/j.eswa.2020.114316.
  • Daw, A., A. Karpatne, W. Watkins, J. Read, and V. Kumar. 2017. Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling. Machine Learning. doi:10.48550/arXiv.1710.11431.
  • Depina, I., S. Jain, S. Mar Valsson, and H. Gotovac. 2022. “Application of Physics-Informed Neural Networks to Inverse Problems in Unsaturated Groundwater Flow.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 16 (1): 21–36. doi:10.1080/17499518.2021.1971251.
  • Grantz, W. 2001. “Immersed Tunnel Settlements.” Tunnelling and Underground Space Technology 16 (3): 203–210. doi:10.1016/S0886-7798(01)00040-2.
  • Hu, Z., Y. Xie, G. Xu, S. Bin, H. Liu, and J. Lai. 2018a. “Advantages and Potential Challenges of Applying Semi-Rigid Elements in an Immersed Tunnel: A Case Study of the Hong Kong-Zhuhai-Macao Bridge.” Tunnelling and Underground Space Technology 79: 143–149. doi:10.1016/j.tust.2018.05.004.
  • Hu, Z., Y. Xie, G. Xu, S. Bin, H. Zhang, H. Lai, H. Liu, and C. Yan. 2018b. “Segmental Joint Model Tests of Immersed Tunnel on a Settlement Platform: A Case Study of the Hongkong-Zhuhai-Macao Bridge.” Tunnelling and Underground Space Technology 78: 188–200. doi:10.1016/j.tust.2018.03.020.
  • Ingerslev, C. 2010. “Immersed and Floating Tunnels.” Procedia Engineering 4: 51–59. doi:10.1016/j.proeng.2010.08.007.
  • Karpatne, A., G. Atluri, J. H. Faghmous, M. Steinbach, A. Banerjee, A. Ganguly, S. Shekhar, N. Samatova, and V. Kumar. 2017. “Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data.” IEEE Transactions on Knowledge and Data Engineering 29 (10): 2318–2331. doi:10.1109/TKDE.2017.2720168.
  • Keilholz, S. D., M. E. Magnuson, W. J. Pan, M. Willis, and G. J. Thompson. 2013. “Dynamic Properties of Functional Connectivity in the Rodent.” Brain Connectivity 3 (1): 31–40. doi:10.1089/brain.2012.0115.
  • Kiyomiya, O., M. Nakamichi, H. Yokota, and S. Shiraishi. 2004. “New Type Flexible Joint for the Osaka Port Yumeshima Tunnel Osamu Kiyomiya.” In OCEANS ‘04. MTTS/IEEE TECHNO-OCEAN ‘04 4:2086–2091. Kobe, Japan: IEEE. doi:10.1109/OCEANS.2004.1406465.
  • Kucharczyková, B., P. Daněk, D. Kocáb, and P. Misák. 2017. “Experimental Analysis on Shrinkage and Swelling in Ordinary Concrete.” Advances in Materials Science and Engineering 2017: 1–11. doi:10.1155/2017/3027301.
  • Li, T., H. Shen, Q. Yuan, X. Zhang, and L. Zhang. 2017. “Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach.” Geophysical Research Letters 44 (23): 11–985. doi:10.1002/2017gl075710.
  • Lin, W., M. Lin, X. Liu, H. Yin, and J. Gao. 2022. “Novelties in the Islands and Tunnel Project of the Hong Kong–Zhuhai–Macao Bridge.” Tunnelling and Underground Space Technology 120. doi:10.1016/j.tust.2021.104287.
  • Lin, M., W. Lin, H. Yin, X. Liu, and K. Liu. 2019. “Memory Bearing: A Novel Solution to Protect Element Joints from Differential Settlement for Immersed Tunnels with Deep Alignment.” Tunnelling and Underground Space Technology 88: 144–155. doi:10.1016/j.tust.2019.03.004.
  • McInnes, K.L., White, C.J., Haigh, I.D., Hemer, M.A., Hoeke, R.K., Holbrook, N.J., Kiem, A.S., Oliver, E.C., Ranasinghe, R., Walsh, K.J. and Westra, S. 2016. “Natural Hazards in Australia: Sea Level and Coastal Extremes.” Climatic Change 139 (1): 69–83. doi:10.1007/s10584-016-1647-8.
  • Mokhtari, F., M. I. Akhlaghi, S. L. Simpson, G. Wu, and P. J. Laurienti. 2019. “Sliding Window Correlation Analysis: Modulating Window Shape for Dynamic Brain Connectivity in Resting State.” Neuroimage 189: 655–666. doi:10.1016/j.neuroimage.2019.02.001.
  • Nakazawa, R., Y. Minamoto, N. Inoue, and M. Tanahashi. 2022. “Species Reaction Rate Modelling Based on Physics-Guided Machine Learning.” Combustion and Flame 235. doi:10.1016/j.combustflame.2021.111696.
  • Papadimitriou, S., J. Sun, and P. S. Yu. 2006. “Local Correlation Tracking in Time Series.” Sixth International Conference on Data Mining (ICDM'06), 456–465.
  • Phoon, K.-K., and W. Zhang. 2022. “Future of Machine Learning in Geotechnics.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. doi:10.1080/17499518.2022.2087884.
  • Quanke, S., Z. Yongling, C. Yue, F. Lei, Y. Yu, S. Zongxian, H. de Wit, and L. Ying. 2022. “Hong Kong Zhuhai Macao Bridge-Tunnel Project Immersed Tunnel and Artificial Islands – from an Owners’ Perspective.” Tunnelling and Underground Space Technology 121. doi:10.1016/j.tust.2021.104308.
  • Rezaee, M., S. F. F. Mojtahedi, E. Taherabadi, K. Soleymani, and M. Pejman. 2021. “Prediction of Shear Strength Parameters of Hydrocarbon Contaminated Sand Based on Machine Learning Methods.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 15 (4): 317–335. doi:10.1080/17499518.2020.1861633.
  • Snoek, J., H. Larochelle, and R. P. Adams. 2012. “Practical Bayesian Optimization of Machine Learning Algorithms.” Paper Presented at the Advances in Neural Information Processing Systems 25.
  • Tidlund, M., J. Spross, and S. Larsson. 2022. “Observational Method as Risk Management Tool: The Hvalfjörður Tunnel Project, Iceland.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards. doi:10.1080/17499518.2022.2046784.
  • Wang, M., and J. Cao. 2022. “An Automatic Identification Method of Marine Magnetic Anomalies Based on the Sliding Window Correlation Coefficient Method.” Journal of Applied Geophysics 205. doi:10.1016/j.jappgeo.2022.104761.
  • Wang, Z., B. Hu, B. Huang, Z. Ma, A. Biswas, Y. Jiang, and Z. Shi. 2022. “Predicting Annual PM2.5 in Mainland China from 2014 to 2020 Using Multi Temporal Satellite Product: An Improved Deep Learning Approach with Spatial Generalization Ability.” ISPRS Journal of Photogrammetry and Remote Sensing 187: 141–158. doi:10.1016/j.isprsjprs.2022.03.002.
  • Wang, J., Y. Li, R. Zhao, and R. X. Gao. 2020. “Physics Guided Neural Network for Machining Tool Wear Prediction.” Journal of Manufacturing Systems 57: 298–310. doi:10.1016/j.jmsy.2020.09.005.
  • Xiao, W., H. Yu, Y. Yuan, L. Taerwe, and R. Chai. 2015. “Compression–Bending Behavior of a Scaled Immersion Joint.” Tunnelling and Underground Space Technology 49: 426–437. doi:10.1016/j.tust.2015.06.002.
  • Xiao, W., H. Yu, Y. Yuan, L. Taerwe, and G. Xu. 2017. “Compression-shear Behavior of a Scaled Immersion Joint with Steel Shear Keys.” Tunnelling and Underground Space Technology 70: 76–88. doi:10.1016/j.tust.2017.07.007.
  • Xu, X., L. Tong, S. Liu, and H. Li. 2019. “Evaluation Model for Immersed Tunnel Health State: A Case Study of Honggu Tunnel, Jiangxi Province, China.” Tunnelling and Underground Space Technology 90: 239–248. doi:10.1016/j.tust.2019.05.005.
  • Xue, Y., B. Zhou, S. Ge, D. Qiu, and H. Gong. 2020. “Optimum Design Calculation Method for the Reasonable Buried Depth: A Case Study from Hong Kong-Zhuhai-Macao Immersed Tunnel.” Ocean Engineering 206. doi:10.1016/j.oceaneng.2020.107275.
  • Zhang, W., C. Wu, Y. Li, L. Wang, and P. Samui. 2021. “Assessment of Pile Drivability Using Random Forest Regression and Multivariate Adaptive Regression Splines.” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 15 (1): 27–40. doi:10.1080/17499518.2019.1674340.
  • Zhou, H., L. Wang, B. Jiang, and Y. Wang. 2022. “Improved Vertical Displacement Calculation Model for Immersed Tube Tunnel Considering Tidal Load.” Marine Georesources & Geotechnology. doi:10.1080/1064119x.2021.1962458.
  • Zhu, X., J. Chu, K. Wang, S. Wu, W. Yan, and K. Chiam. 2021. “Prediction of Rockhead Using a Hybrid N-XGBoost Machine Learning Framework.” Journal of Rock Mechanics and Geotechnical Engineering 13 (6): 1231–1245. doi:10.1016/j.jrmge.2021.06.012.
  • Zhu, H., A. Garg, X. Yu, and H. W. Zhou. 2022. “Editorial for Internet of Things (IoT) and Artificial Intelligence (AI) in Geotechnical Engineering.” Journal of Rock Mechanics and Geotechnical Engineering 14 (4): 1025–1027. doi:10.1016/j.jrmge.2022.07.001.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.