Affiliation:
1. Department of Civil Engineering, Central South University, Changsha 410075, China
2. National Engineering Research Center of Geological Disaster Prevention Technology in Land Transportation, Southwest Jiaotong University, Chengdu 611731, China
3. Sichuan Expressway Construction & Development Group Co., Ltd., Chengdu 610041, China
Abstract
The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.
Funder
National Natural Science Foundation of China
Team Building Project of National Engineering Research Center of Geological Disaster Prevention Technology in Land Transportation
Sichuan Highway Construction and Development Group Co.
Reference76 articles.
1. Effects of molding water content and compaction degree on the microstructure and permeability of compacted loess;Li;Acta Geotech.,2023
2. A framework for determining the optimal moisture content of high-speed railway-graded aggregate materials based on the lab vibration compaction method;Xie;Constr. Build. Mater.,2023
3. Laboratory investigation on parameter optimization of vibrating compaction for high-speed railway’s Group B;Ye;J. Railw. Sci. Eng.,2021
4. Experimental investigation of macro-and meso-scale compaction characteristics of unbound permeable base materials;Wang;Chin. J. Rock. Mech. Eng.,2022
5. Xiao, X., Li, T., Lin, F., Li, X., Hao, Z., and Li, J. (2024). A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN. Sensors, 24.