在多因素合效应下,影响机制探索和机器学习预测洛斯压缩变形系数
Wei Zhou1,2,3, Changqing Deng1,2,3, Jin Wang1,2,3
1College of Civil and Architectural Engineering, Shaoyang University, Shaoyang, Hunan, China.
PloS one
|January 9, 2026
概括
预测料填充器的压缩变形系数对于底层稳定性至关重要. 优化的机器学习模型,特别是Sparrow搜索算法优化的反向传播神经网络 (SSA-BP),准确地预测了这个系数,提高了工程安全.
科学领域:
- 地质技术工程 地质技术工程
- 计算力学 计算力学 计算力学
- 材料科学 是一种材料科学.
背景情况:
- 洛斯次级工程稳定性依赖于准确的压缩变形系数预测.
- 影响压缩的关键因素包括压缩,含水量,垂直压力和成型方法.
研究的目的:
- 为了研究各种因素对洛斯压缩特性的影响.
- 开发和比较用于预测压缩变形系数的机器学习模型.
- 通过使用SHAP可解释性来分析特征的重要性和相互作用.
主要方法:
- 在不同条件下对洛斯压缩的实验研究.
- 开发了XGBoost (XGB),支持向量回归 (SVR),逆向传播神经网络 (BP) 和SSA-BP模型.
- 应用SHAP分析用于特征贡献和合效应量化.
主要成果:
- 振动压缩,增加压缩和减少水含量通过增强粒子相互锁定来降低压缩变形系数.
- 该系数随着压缩物中的垂直压力上升而显著增加.
- SSA-BP模型实现了最高的准确性 (RMSE为0.138%),性能优于SVR,XGB和标准BP模型.
结论:
- 该SSA-BP模型提供了一个可靠的工具,用于预测压缩变形系数在loess次级工程.
- 垂直压力是最有影响力的因素,观察到显著的非线性相互作用,特别是与含水量.
- 结果为改进洛斯亚设计和稳定性评估提供了机械洞察力.
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