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

ABSTRACT

Due to complex interactions between immersed tunnel and surrounding environment, it is difficult to apply theoretical analysis for axial displacement (DIS) of immersion joints. To develop a generalised model for DIS prediction, Deep Neural Network (DNN) could be considered. However, the spatial generalisation of black-box DNN models is not always convincible for small data. In this study, we proposed a novel hybrid physical data (HPD) informed DNN model with improved spatial generalisation for prediction of DIS. The physical mechanism of DIS is firstly analysed by correlation between DIS and other monitoring data. The HPD is then created based on the physical analysis and contributes to the DNN as a substituting feature rather than an additional feature. Three DNN models fed with different groups of features are compared, while the proposed HPD-DNN has outperformed others in terms of both prediction generalisation as well as accuracy. The permutation feature importance analysis reveals that HPD has effectively enhanced physical interpretation of DNN, which supports the results stated in physical analysis. The application of HPD is further verified to enhance the spatial generalisation of prediction for not only DNN but also other black-box models, which is promising for insufficient data problems in geotechnical engineering.

KEYWORDS:

• Immersed tunnel
• axial displacement
• deep neural network
• hybrid physical data
• spatial generalisation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The partial codes and data that support the findings of this study are openly available in GitHub at https://github.com/umgeotech/Algorithm/tree/master/Axial%20Displacement%20Prediction.

Additional information

Funding

The authors greatly acknowledge the financial support from Ministry of Science and Technology of the People’s Republic of China [grant number 2019YFB1600700] and Guangdong Provincial Department of Science and Technology [grant number 2019B111106001], the Science and Technology Development Fund, Macao SAR (File nos. 0026/2020/AFJ and SKL-IOTSC(UM)−2021–2023) and the Funds for International Cooperation and Exchange of the National Natural Science Foundation of China [grant number 52061160367]. The field monitoring data was partially supported by the Hong Kong-Zhuhai-Macao Bridge National Field Scientific Observation and Research Station for Material Corrosion and Structural Safety. This work was also performed in part at SICC which is supported by SKL-IOTSC, University of Macau.