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Chin. Phys. B, 2023, Vol. 32(4): 046203    DOI: 10.1088/1674-1056/acb76a
Special Issue: SPECIAL TOPIC — Smart design of materials and design of smart materials
SPECIAL TOPIC—Smart design of materials and design of smart materials Prev   Next  

Fast prediction of the mechanical response for layered pavement under instantaneous large impact based on random forest regression

Ming-Jun Li(励明君)1, Lina Yang(杨哩娜)1, Deng Wang(王登)1, Si-Yi Wang(王斯艺)1, Jing-Nan Tang(唐静楠)1, Yi Jiang(姜毅)1,†, and Jie Chen(陈杰)2,‡
1 Laboratory of Aerospace Launching Technology, School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China;
2 Center for Phononics and Thermal Energy Science, China-EU Joint Laboratory for Nanophononics, Ministry of Education Key Laboratory of Advanced Micro-structured Materials, School of Physics Science and Engineering, Tongji University, Shanghai 200092, China
Abstract  The layered pavements usually exhibit complicated mechanical properties with the effect of complex material properties under external environment. In some cases, such as launching missiles or rockets, layered pavements are required to bear large impulse load. However, traditional methods cannot non-destructively and quickly detect the internal structural of pavements. Thus, accurate and fast prediction of the mechanical properties of layered pavements is of great importance and necessity. In recent years, machine learning has shown great superiority in solving nonlinear problems. In this work, we present a method of predicting the maximum deflection and damage factor of layered pavements under instantaneous large impact based on random forest regression with the deflection basin parameters obtained from falling weight deflection testing. The regression coefficient R2 of testing datasets are above 0.94 in the process of predicting the elastic moduli of structural layers and mechanical responses, which indicates that the prediction results have great consistency with finite element simulation results. This paper provides a novel method for fast and accurate prediction of pavement mechanical responses under instantaneous large impact load using partial structural parameters of pavements, and has application potential in non-destructive evaluation of pavement structure.
Keywords:  deflection basin parameters      pavement condition assessment      instantaneous large impact      random forest regression  
Received:  29 December 2022      Revised:  20 January 2023      Accepted manuscript online:  31 January 2023
PACS:  62.20.-x (Mechanical properties of solids)  
  46.15.-x (Computational methods in continuum mechanics)  
Fund: Project supported in part by the National Natural Science Foundation of China (Grant No. 12075168) and the Fund from the Science and Technology Commission of Shanghai Municipality (Grant No. 21JC1405600).
Corresponding Authors:  Yi Jiang, Jie Chen     E-mail:;

Cite this article: 

Ming-Jun Li(励明君), Lina Yang(杨哩娜), Deng Wang(王登), Si-Yi Wang(王斯艺), Jing-Nan Tang(唐静楠), Yi Jiang(姜毅), and Jie Chen(陈杰) Fast prediction of the mechanical response for layered pavement under instantaneous large impact based on random forest regression 2023 Chin. Phys. B 32 046203

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