使用机器学习工具评估纳米化飞灰处理的粘土土的不受限制的压力强度
Anish Kumar1, Sanjeev Sinha2, Rojee Pradhananga3
1Department of Civil Engineering, Rajkiya Engineering College, Azamgarh, Uttar Pradesh, India.
Scientific reports
|October 1, 2025
概括
梯度增强机 (GBM) 准确预测纳米化土壤中的无限制压力 (UCS). 这种机器学习方法提高了地质工程中的土壤稳定性预测.
科学领域:
- 地质技术工程 地质技术工程
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 准确预测无限制压力 (UCS) 对地质技术应用中的土壤稳定至关重要.
- 评估机器学习模型来预测纳米剂飞灰强化粘土土中的UCS是一个活跃的研究领域.
- 提高模型的可解释性以及预测准确性对于实际的工程应用是必不可少的.
研究的目的:
- 为了比较评估四种机器学习模型的预测性能:梯度提升机 (GBM),随机决策森林 (RDF),非参数回归 (NP) 和决策树 (TREE).
- 研究集合学习与敏感性,单调性和SHAP分析相结合的应用,以改善UCS预测和可解释性.
- 为了确定影响UCS的关键变量在纳米化飞灰强化粘土土中.
主要方法:
- 训练和验证GBM,RDF,NP和TREE模型,使用固化日,最大干密度 (MDD),最佳水分含量 (OMC),飞灰,多壁碳纳米管 (MWCNT) 和六甲 (SHMP) 的数据集.
- 使用统计指标 (R2,MAE,MSE),泰勒图,REC曲线和AOC分析进行性能评估.
- 使用灵敏度,单调性和SHAP分析来确定影响因素和验证关系.
主要成果:
- 在预测UCS方面,GBM表现出卓越的表现,在培训和测试阶段实现了最高的R2值和最低的错误指标 (MAE,MSE).
- 敏感性和SHAP分析确定治疗日,MDD和SHMP是影响UCS的最重要因素.
- 单调性分析证实了与 Days 和 MDD 的正相关性,与 OMC 和 SHMP 的反相关性.
结论:
- 渐变增强机 (GBM) 已被确立为一个高度准确和可解释的模型,用于预测纳米化飞灰增强粘土土的无限制压力 (UCS).
- 集体学习与先进的分析技术 (灵敏度,单调性,SHAP) 的整合显著提高了地质工程中的预测能力和解释能力.
- 这些发现为优化建筑和地质工程项目中的土壤稳定策略提供了宝贵的工具.
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