主要组件分析 - 基于人工神经网络的模型,用于预测季节性结土壤的静态强度
Yiqiang Sun1,2, Shijie Zhou3, Shangjiu Meng3,4,5
1College of Civil Engineering and Architecture, Harbin University of Science and Technology, Harbin, 150080, China. syq_iem@163.com.
Scientific reports
|September 26, 2023
概括
机器学习模型预测土壤静态强度降低由于结解周期. PCA-ANN算法为季节性结土壤中的土壤特性预测提供了更高的准确性.
科学领域:
- 地质技术工程 地质技术工程
- 土壤力学 土壤力学
- 人工智能的人工智能
背景情况:
- 季节性结的土壤经历每年融周期,导致机械性能恶化.
- 准确预测土壤静态强度对于在寒冷地区的工程应用至关重要.
研究的目的:
- 开发和比较机器学习模型,以预测融条件下的土壤静态强度.
- 调查关键因素对土壤静态强度恶化的影响.
- 为提高准确性提出一个增强的预测模型.
主要方法:
- 利用了六个关键因素:水分含量,压缩程度,限制压力,结温度,结解周期和解时间.
- 比较支持向量机 (SVM),随机森林 (RF) 和人工神经网络 (ANN) 算法.
- 开发了一个主要组件分析与ANN (PCA-ANN) 算法相结合,用于增强预测.
主要成果:
- 人工神经网络 (ANN) 显示出比SVM和RF更高的精度.
- PCA-ANN算法进一步提高了对土壤静态强度的预测精度.
- 在最初的冷解周期后,土壤静态强度显著下降,然后平稳.
- 强度的降低加剧了较高的水分含量和压缩度.
结论:
- PCA-ANN模型提供了一种可靠的方法来预测季节性结土壤的静态强度.
- 研究结果为在寒冷环境中实施实际工程项目提供了宝贵的科学见解.
- 这项研究强调了机器学习在理解复杂的土壤行为的有效性.
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