可解释的集体学习和多层感知子建模用于预测超高性能混凝土的压力强度
Yaren Aydın1, Celal Cakiroglu2, Gebrail Bekdaş1
1Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey.
Biomimetics (Basel, Switzerland)
|September 27, 2024
概括
机器学习模型准确地预测了超高性能混凝土的压力强度. 集体堆叠回归模型的表现优于多层感知器,混凝土的年龄和材料组成是关键预测因素.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 超高性能混凝土 (UHPC) 提供了增强的耐用性,并使更薄的结构元素.
- 预测UHPC压力强度对于材料设计和应用至关重要.
- 确定压力强度的传统方法是耗时的.
研究的目的:
- 开发和比较用于预测UHPC压力强度的机器学习模型.
- 通过特征重要性和SHAP分析来解释开发的模型.
- 评估数据分割策略对模型性能的影响.
主要方法:
- 使用多层感知子 (MLP) 和一个整体堆叠回归器模型.
- 堆积回归器整合了极端梯度提升 (XGBoost),类别提升 (CatBoost),轻梯度提升机 (LightGBM) 和额外树木回归器的输出.
- 研究了训练和测试数据集的不同随机分割的影响.
主要成果:
- 堆叠回归器在测试组中获得了0.971的R平方平均优异得分.
- 在MLP模型中,平均R平方得分为0.909.
- 特性重要性分析显示,混凝土的年龄,烟,纤维,超塑剂,水泥,聚合物和水含量显著影响预测.
结论:
- 集成机器学习,特别是堆叠回归器,提供了一个非常准确的方法来预测UHPC压力强度.
- 通过SHAP分析的模型解释性突出了影响混凝土性能的关键材料组件和年龄.
- 这些发现支持使用机器学习来有效地表征混凝土技术中的材料.
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