基于机器学习的含有矿物添加剂的混凝土柔性强度的研究
1Key Laboratory of Urban Security and Disaster Engineering of Ministry of Education, Beijing Key Laboratory of Earthquake Engineering and Structural Retrofit, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing, 100124, China.
Scientific reports
|October 23, 2023
概括
机器学习可以准确地预测混凝土的屈曲强度. 水结合比对强度有负面影响,而维护年龄对强度有积极影响. 补充的水泥材料对强度产生复杂的影响.
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:
- 混凝土的弹性强度对于结构完整性至关重要.
- 补充性水泥材料 (SCM),如二氧化烟雾,飞灰和渣渣,用于增强混凝土的性能.
- 准确预测屈曲强度对于优化混凝土混合设计至关重要.
研究的目的:
- 研究用于预测含有SCM的混凝土曲强度的机器学习模型.
- 用可解释的人工智能分析关键参数对强度的影响.
- 了解SCM剂量与混凝土屈曲强度之间的复杂关系.
主要方法:
- 使用线性回归 (LR),随机森林 (RF) 和极端梯度增强 (XGB) 模型进行预测.
- 采用SHAP模型来分析输入参数对屈曲强度的影响.
- 开发基于具体特征的全面数据库的预测模型.
主要成果:
- 预测准确性从LR到RF到XGB模型逐渐得到改善.
- 水结合剂比被确定为最有影响力的参数,显示与屈曲强度有负相关性.
- 维护年龄与屈曲强度正相关,仅次于水结合剂比率的影响.
结论:
- 机器学习模型可以准确地预测混凝土的屈曲强度.
- 对SCM的最佳剂量至关重要;超过某些值可能会降低屈曲强度.
- 了解参数相互作用,如水粘合比和SCM含量,是混凝土性能的关键.
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