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使用人工神经网络预测凝聚性和非凝聚性土壤的弹性模块值
1Institute of Civil Engineering, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warszawa, Poland.
Materials (Basel, Switzerland)
|November 9, 2024
概括
人工神经网络 (ANN) 准确地预测了用于路面设计的土壤的弹性模量. 这种人工智能方法为传统实验室测试提供了可靠的替代方案,提高了道路结构效率.
科学领域:
- 地质技术工程 地质技术工程
- 人工智能的人工智能
- 铺路工程 铺路工程 铺路工程
背景情况:
- 弹性模量 (M) 对路面设计至关重要,但传统的实验室测试耗时.
- 准确的M预测对于优化路面性能和耐用性至关重要.
研究的目的:
- 应用人工神经网络 (ANN) 来预测亚和亚基土壤的弹性模量 (M).
- 在M预测中评估ANN模型的准确性和可解释性.
- 探索ANN作为传统实验室测试的实际替代方案.
主要方法:
- 用一个包含1683M观测的数据集与一个包含8个输入变量的ANN模型一起使用.
- 模型优化使用准牛顿方法进行.
- 用SHAP (夏普利添加式解释) 分析来确定模型的可解释性.
主要成果:
- 该ANN模型实现了0.9613.2的高确定系数 (R^2).
- 该模型在选择和测试数据集上都显示了较低的错误率.
- SHAP分析确定了和率,可塑性指数和土壤分级作为关键影响的参数.
结论:
- ANNs提供了一个实用和准确的工具来估计弹性模量.
- 这种人工智能驱动的方法可以增强路面设计流程,提高道路结构效率.
- 该研究验证了ANN作为实验室测试的可行替代方案来确定M.
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