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使用机器学习技术预测冷土壤的解沉积系数
Ke Wang1,2, Shuang Liu3,4, Wenqiang Xing1,2
1College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518060, China.
Scientific reports
|December 30, 2025
概括
这项研究预测了冷土壤的解沉积系数 (TSC),这是解变形的关键指标. 人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 模型根据结温度,干燥密度,水含量和覆盖负载准确预测TSC.
科学领域:
- 地质技术工程 地质技术工程
- 土壤力学 土壤力学
- 永久土科学 永久土科学
背景情况:
- 解沉积系数 (TSC) 对于评估冷土壤解变形至关重要.
- 了解环境和负载因素对TSC的影响对于寒冷地区的基础设施稳定性至关重要.
研究的目的:
- 研究结温度,干燥密度,含水量,覆盖负荷和冷土壤的TSC之间的关系.
- 为TSC开发和验证准确的非线性预测模型.
- 确定影响TSC最有影响的因素,并确定最佳预测模型.
主要方法:
- 采用非线性预测方法,使用841个数据样本的实验数据库.
- 采用人工神经网络 (ANN) 和自适应神经模糊推理系统 (ANFIS) 模型.
- 进行敏感性分析和相互信息 (MI) 分析,以评估参数影响和模型性能.
主要成果:
- ANN和ANFIS模型在预测冷土壤TSC方面表现出卓越的准确性.
- 通过对新的数据集进行验证,ANFIS模型显示出可靠性,满足工程要求.
- 相互信息分析量化了各种因素对TSC的影响.
结论:
- 非线性模型,特别是ANFIS,对于预测冷土壤TSC有效.
- 结温度,干燥密度,含水量和覆盖负载显著影响TSC.
- 这项研究为预测结土壤在解条件下的行为提供了宝贵的见解.
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