机器学习方法用于根据地下土壤特性预测灵活路面的结构数量
1Department of Civil Engineering, Ghazni Technical University, Ghazni, 2301, Afghanistan. asadullah.ziar@teug.edu.af.
Scientific reports
|August 5, 2025
概括
机器学习使用土壤和环境数据准确预测柔性路面结构数. 梯度增强模型提供了高效的路面设计,减少了对广泛实验室测试的依赖.
科学领域:
- 土木工程 土木工程是指土木工程.
- 地质技术工程 地质技术工程
- 机器学习应用 机器学习应用
背景情况:
- 路面设计依赖于准确的结构数字预测.
- 传统方法涉及广泛的实验室测试.
- 底层土壤特性和环境因素对路面性能有很大的影响.
研究的目的:
- 开发和评估用于预测灵活路面结构数量的机器学习模型.
- 确定路面设计的关键土壤和环境预测因素.
- 为传统路面设计方法提供更有效的替代方案.
主要方法:
- 利用了四个机器学习算法:随机森林,极端梯度增强,梯度增强和K最近邻居.
- 使用美国州公路和运输官员协会1993年设计方程将弹性模块转换为结构数字.
- 输入变量包括水分含量,干单位重量,可塑性指数和冷解周期.
主要成果:
- 梯度提升实现了最高的精度,确定系数为0.917.
- 在大多数模型中,水分含量被确定为最重要的预测因素.
- 机器学习模型在预测结构数字方面表现出很高的准确性.
结论:
- 机器学习为预测路面结构数字提供了准确有效的方法.
- 这种方法减少了对昂贵和耗时的实验室测试的需求.
- 该研究强调了机器学习在优化路面工程和设计方面的潜力.
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