一种基于ML-间隔预测理论的全截面评估高速铁路底层压缩质量的新方法
Zhixing Deng1, Wubin Wang2, Linrong Xu1
1Department of Civil Engineering, Central South University, Changsha 410075, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种新的机器学习方法,以准确评估高速铁路底层压缩质量. 该方法通过精确预测整个下层部分的最大干燥密度和压缩程度来提高操作安全性.
科学领域:
- 地质技术工程 地质技术工程
- 铁路工程 铁路工程是指铁路工程.
- 机器学习应用 机器学习应用
背景情况:
- 高速铁路底层压缩质量对于运营安全至关重要.
- 目前用于确定最大干密度 (ρmax) 的方法缺乏智能和准确性.
- 不充分的压缩评估可能会损害铁路基础设施的完整性.
研究的目的:
- 提出一种新的智能方法,用于全面评估高速铁路底层压缩质量.
- 为了提高预测最大干密度 (ρmax) 的准确性和可靠性.
- 开发一个可靠的模型来评估压缩度 (K) 分布.
主要方法:
- 室内振动压缩测试通过动态刚度 (K) 转折点来确定 ρmax.
- 开发一个整合BPNN,SVR和RF的Pso-OptimalML-Adaboost (POA) 模型,用于 ρmax 预测.
- 应用ML间隔预测理论和Bootstrap-POA-ANN来量化预测不确定性和全部分分析.
主要成果:
- 该PSO-BPNN-AdaBoost模型在 ρmax 预测方面表现出卓越的准确性.
- 引导式-POA-ANN模型有效地产生了对 ρmax 的可靠预测间隔.
- 全截面评估模型为压缩度 (K) 提供了空间分布间隔,确定了最佳压缩厚度 (H0).
结论:
- 拟议的ML间隔预测方法在评估高速铁路底层压缩质量方面取得了重大进展.
- 精确的 ρmax 预测和全截面 K 分布分析有助于改进下层工程实践.
- 这些发现支持提高高速铁路网络的运营安全性和效率.
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