A new method of rapid-intelligent assessment for the dynamic service performance in high-speed railway subgrade

ABSTRACT

There is a lack of a rapid, advanced, and comprehensive method for assessing the dynamic service performance in high-speed railway subgrade, which leads to an inability to pre-assess the health state of the subgrades in advance. In this paper, an innovative method of rapid-intelligent assessment for the dynamic service performance (RADSP) in high-speed railway subgrade was proposed. Firstly, a large-scale exciter was used to simulate the train loading effect, which could rapidly obtain the assessment metrics for subgrade dynamic service performance, including subgrade dynamic response (SDR) and subgrade cumulative deformation (SCD), where the SDR includes dynamic stress and acceleration. Then, to explore the future development trend of SCD during operation, an intelligent and advanced prediction of SCD was conducted using the optimal machine learning (ML) prediction model. Finally, by combining the results of the SDR and SCD assessments to achieve the RADSP in high-speed railway subgrade. Moreover, the method was applied to assess the dynamic service performance of diatomaceous earth subgrade structures with four different waterproofing structure layers (WSL): the middle of subgrade surface layer (WSL-1), the bottom of subgrade surface layer (WSL-2), the middle of subgrade bottom layer (WSL-3), and the bottom of subgrade bottom layer (WSL-4). The results indicate that: 1) During the excitation experiments, the maximum values for SDR (dynamic stress, acceleration) and SCD exceed the respective limits of 50 kPa, 10 m/s², and 15 mm under WSL-1 and WSL-4. 2) The ensemble model, AdaBoost-GPR, is selected as the optimal ML model to predict the SCD values. Notably, the prediction SCD value exceeds the 15 mm limit under WSL-3, indicating that the SCD during future operation does not meet the requirements for pre-assessment. 3) Under WSL-2, both SDR and SCD assessments satisfy the limit value requirements, indicating that the dynamic service performance is better. Based on the proposed method, it is found that when designing waterproof-drainage subgrade structures in diatomaceous earth areas, it is recommended to position the WSL at the bottom of subgrade surface layer. The research findings serve as a crucial reference and valuable lesson for the health monitoring of high-speed railway subgrades.

KEYWORDS:

• High-speed railway
• excitation experiments
• subgrade dynamic response
• subgrade cumulative deformation
• machine learning
• performance assessment

Disclosure statement

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

Additional information

Funding

The present work has been supported by the National Natural Science Foundation of China (U2268213 and 51978588).