开发可解释的机器学习-沙普利增材解释模型,用于用地质聚合物稳定凝聚性土壤不受限制的压力强度
Anh Quan Ngo1, Linh Quy Nguyen2, Van Quan Tran2
1Hydraulic Construction Institute-Vietnam Academy For Water Resources, Hanoi, Vietnam.
PloS one
|June 8, 2023
概括
这项研究开发了一种可解释的人工神经网络 (ANN) 模型,以预测地质聚合物稳定凝聚土的无限制压力 (UCS). 该ANN模型取得了卓越的准确性,识别了影响UCS的关键因素,例如地面颗粒爆破渣含量.
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
- 机器学习 机器学习
背景情况:
- 地质聚合物为土壤稳定提供了一个可持续的替代方案.
- 预测稳定土壤的无限制压力强度 (UCS) 对工程应用至关重要.
- 可解释的机器学习模型可以提高对土壤稳定机制的理解.
研究的目的:
- 开发和比较可解释的机器学习 (ML) 模型,用于预测用地质聚合物稳定凝聚性土壤的28天UCS.
- 为了确定UCS预测的最佳ML模型.
- 用灵敏度分析来确定各种输入参数对UCS的影响.
主要方法:
- 使用282个土壤样本的数据库开发了四个ML模型 (随机森林,人工神经网络,极端梯度增强,梯度增强).
- 使用粒子群优化和K折交叉验证进行了超参数调整.
- 沙普利添加式解释 (SHAP) 用于敏感性分析.
主要成果:
- 人工神经网络 (ANN) 模型在R2 = 0.9808,RMSE = 0.8808 MPa和MAE = 0.6344 MPa的情况下表现出卓越的性能.
- 灵敏度分析显示,土壤颗粒爆炸渣含量 (GGBFS) 和液体限量 (LL) 是最有影响力的参数.
- GGBFS显示出正相关性,而LL与UCS显示出负相关性.
结论:
- 该ANN模型提供了一个准确和可解释的工具,用于预测地质聚合物稳定凝聚土的UCS.
- 了解GGBFS和LL等输入参数的影响对于优化土壤稳定至关重要.
- 这项研究有助于推进可持续的地质工程实践.
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