通过随机森林预测纤维增强UHPC的屈曲强度
Keldys D Sarmiento-Pupo1, Jesús D Escalante-Tovar1, Jesús E Altamiranda1
1Department of Civil and Environmental Engineering, Universidad del Norte, Barranquilla, Colombia.
Scientific reports
|November 26, 2025
概括
预测纤维增强超高性能混凝土 (UHPC) 的屈曲强度是复杂的. 这项研究使用随机森林回归来准确地模拟UHPC的柔性行为,使用各种纤维和补充水泥材料组合.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 计算力学 计算力学 计算力学
背景情况:
- 超高性能混凝土 (UHPC) 提供卓越的机械性能,特别是柔性强度和柔性,由于纤维钢筋.
- 预测纤维增强UHPC的行为是具有挑战性的,因为复杂的材料相互作用,特别是补充性水泥材料 (SCM) 改变微观结构.
- 现有的模型很难用多种SCM和多种纤维类型来捕捉UHPC的细微性能.
研究的目的:
- 开发一款用于纤维增强UHPC的最终屈曲强度的预测模型.
- 研究各种SCM和纤维类型对UHPC柔性性能的影响.
- 提供可解释的洞察力,了解控制UHPC弹性行为的关键因素.
主要方法:
- 利用随机森林 (RF) 回归,一种机器学习算法,用于预测.
- 在550个实验UHPC混合物的广泛数据集上训练并验证了射频模型.
- 采用部分依赖图 (PDP) 来可视化和解释输入变量对屈曲强度的影响.
主要成果:
- 射频模型成功地以高精度预测了UHPC的最终屈曲强度.
- 确定了纤维类型和剂量作为影响强度的关键因素.
- 确定了矩阵参数的显著影响,包括水泥含量,二氧化烟雾,水与粘合剂的比率和聚合物大小.
结论:
- 随机森林回归是一种有效的工具,用于预测复杂的UHPC配方的屈曲强度.
- 纤维特征和矩阵组成是HHPC柔性性能的主要驱动因素.
- 该研究为优化用于结构应用的UHPC混合设计提供了有价值,可解释的数据.
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