估计在最佳紧缩状态下用水泥和石灰稳定土壤的强度,使用基于集体的多重机器学习
Kennedy C Onyelowe1,2,3, Arif Ali Baig Moghal4, Ahmed Ebid5
1Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria. konyelowe@mouau.edu.ng.
Scientific reports
|July 3, 2024
概括
这项研究使用水泥和石灰增强了凝聚性土壤强度,机器学习模型准确地预测了不受限制的压力强度 (UCS). 梯度提升和K-最近邻居实现了95%的准确性,突出了最大干密度和一致性限制等关键因素.
科学领域:
- 地质技术工程 地质技术工程
- 环境地质技术 环境地质技术
- 材料科学 材料科学 材料科学
背景情况:
- 凝聚性土壤需要稳定路面分层和垃圾填埋场层,当不受限制的压力强度 (UCS) 低于200kN/m2时.
- 提高机械性能对于地质技术应用中的结构完整性和环境保护至关重要.
- 机器学习为预测土壤行为和优化材料特性提供了先进的分析工具.
研究的目的:
- 对机器学习模型进行比较评估,以预测用水泥和石灰稳定凝聚性土壤的不受限制的压力强度 (UCS).
- 确定用于土壤稳定分析的最有效的基于集群的机器学习技术.
- 确定影响复合凝聚性土壤UCS的关键因素.
主要方法:
- 使用集群式机器学习分类 (渐变增强,CN2,天真贝叶斯,SVM,SGD,K-NN,决策树,随机森林) 和符号回归 (ANN,RSM).
- 在190个实验数据点上训练并测试模型,考虑输入:水泥,石灰,液体极限,可塑性指数,最佳水分含量和最大干密度.
- 进行了相关性矩阵和灵敏度分析,以确定影响UCS的参数.
主要成果:
- 梯度提升 (GB) 和K-最近邻近 (K-NN) 模型实现了最高的准确性 (95%).
- CN2,支持矢量机 (SVM) 和决策树 (Tree) 模型的准确性约为90%.
- 最大干密度 (MDD),一致性极限 (LL,PI) 和水泥含量显著影响了UCS,而最佳水分含量 (OMC) 的影响是微不足道的.
结论:
- 整体机器学习模型,特别是GB和K-NN,对于预测稳定凝聚性土壤的UCS非常有效.
- 通过专注于MDD,一致性限制和水泥含量,可以实现最佳的土壤稳定.
- 这些发现为实地应用提供了有价值的框架,用于设计使用复合土壤的稳定地质技术结构.
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