Application of a hybrid neural network structure for FWD backcalculation based on LTPP database

ABSTRACT

The road layer modulus backcalculation based on the road deflection basin obtained by the Falling Weight Deflectometer is a key issue in road engineering. Traditional Falling Weight Deflectometer backcalculation method based on Artificial Neural Network has the disadvantages of poor generalisation ability and low convergence accuracy in terms of the dynamic modulus. In this paper, a hybrid neural network structure, combined with Residual Neural Network, Recurrent Neural Network and Wide & Deep (Abbreviated as ResRNN–W&D) structure, was proposed for Falling Weight Deflectometer deflection basin backcalculation. A case study using the United States Long-Term Pavement Performance database verified that the ResRNN–W&D structure can train Falling Weight Deflectometer data on multiple roads together and achieve fast and high-precision convergence, thereby greatly improving the availability of the multi-source heterogeneous data. Moreover, two transfer learning methods for the ResRNN–W&D structure were proposed to improve the divergence issue. It was found that the ResRNN–W&D structure has stronger generalisation ability than traditional Artificial Neural Network.

KEYWORDS:

• Falling weight deflectometer
• backcalculation
• artificial neural network
• transfer learning
• artificial intelligence
• LTPP database

Acknowledgements

The authors would also like to acknowledge the financial support provided by Bureau of Transportation Construction of Jiangsu.

Disclosure statement

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

Additional information

Funding

This research was funded by the National Key Research and Development Project, grant number 2020YFA0714302 and 2020YFB1600102, the National Natural Science Foundation of China, grant number 51878164 and 51922030, Southeast University “Zhongying Young Scholars” Project, Department of Transportation of Shandong Province, grant number 2018B51 and Jiangsu Highway Engineering Maintenance Technology Co., Ltd., Huai'an Highway Administration Office andBureau of Transportation Construction of Jiangsu