Deng Zhixing, Wang Wubin, Xu Linrong, Bai Hao, Tang Hao
Department of Civil Engineering, Central South University, Changsha 410075, China.
National Engineering Research Center of Geological Disaster Prevention Technology in Land Transportation, Southwest Jiaotong University, Chengdu 611731, China.
Sensors (Basel). 2024 Jun 5;24(11):3661. doi: 10.3390/s24113661.
The high-speed railway subgrade compaction quality is controlled by the compaction degree (), with the maximum dry density () serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the 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 based on the dynamic stiffness turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting 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 prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of 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 (), 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 . (2) The Bootstrap-POA-ANN interval prediction model for 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 . Comparing the of 5060 cm and 6070 cm, the compaction quality is better with the of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.
高速铁路路基压实质量由压实度（）控制，最大干密度（）是其计算的关键指标。目前确定的机制和方法仍存在不确定性、低效性和缺乏智能性等问题。这些不足会导致对高速铁路路基压实质量的评估不足，进而影响高速铁路的运营安全。本文提出了一种基于机器学习区间预测理论的高速铁路路基压实质量全断面评估新方法。首先，基于室内振动压实试验，提出了一种基于动刚度转折点确定的方法。其次，基于三种典型的机器学习（ML）算法，即反向传播神经网络（BPNN）、支持向量回归（SVR）和随机森林（RF），确定了用于预测的粒子群优化-最优机器学习-自适应增强（POA）模型。第三，引入区间预测理论来量化预测中的不确定性。最后，基于自助法-POA-人工神经网络区间预测模型和空间插值算法，可以确定全断面的区间分布，并基于压实标准（95%）建立压实质量全断面评估模型。此外，将所提出的方法应用于确定西南地区站内路基试验段的最优压实厚度（）。结果表明：（1）PSO-BPNN-AdaBoost模型在精度和误差指标方面表现更好，被选为预测的POA模型。（2）用于的自助法-POA-人工神经网络区间预测模型可以构建清晰可靠的预测区间。（3）压实质量全断面评估模型可以提供的全断面分布区间。比较5060 cm和6070 cm的，40~50 cm时压实质量更好。研究结果可为评估高速铁路路基压实质量提供有效的技术手段。
