开发机器学习框架,预测混合各种工业副产品的超高性能混凝土的机械性能
Kennedy C Onyelowe1,2, Shadi Hanandeh3, Nestor Ulloa4
1Department of Civil Engineering, College of Engineering and Engineering Technology, Michael Okpara University of Agriculture, Umudike, Nigeria. kennedychibuzor@kiu.ac.ug.
Scientific reports
|July 9, 2025
概括
机器学习使用工业副产品准确预测超高性能混凝土 (UHPC) 的性能. 克斯塔尔模型显示了最高的准确性,有助于可持续的建筑材料开发.
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.
- 可持续建筑 可持续建筑
背景情况:
- 超高性能混凝土 (UHPC) 提供卓越的机械性能和耐久性.
- 将工业副产品纳入UHPC中,为传统水泥提供了一个可持续的替代方案.
- 准确的预测建模对于优化UHPC性能和材料利用至关重要.
研究的目的:
- 开发和评估用于预测UHPC属性的机器学习模型.
- 为了确定压力强度,屈曲强度,可加工性和多孔性的最准确的预测模型.
- 通过灵敏度分析来确定影响UHPC性能的关键参数.
主要方法:
- 评估多个机器学习模型:Kstar,M5Rules,ElasticNet,XNV和决策表 (DT).
- 预测压力强度 (Fc),屈曲强度 (Ff),可加工性 (Slump) 和多孔性.
- 应用SHAP和霍夫曼和花园师方法进行灵敏度分析.
主要成果:
- 在所有评估的UHPC属性 (Fc,Ff,Slump,Porosity) 中,Kstar模型表现出最高的预测准确性.
- 灵敏度分析确定了影响混凝土性能的关键参数,为材料行为提供了洞察力.
- 通过使用工业副产品成功预测UHPC属性.
结论:
- 机器学习,特别是Kstar模型,为包含工业副产品的UHPC提供了准确的预测能力.
- 该研究支持在建筑中可持续使用工业副产品,减少对环境的影响.
- 使用这些预测模型,可以开发优化的UHPC配方,从而促进高效的材料使用.
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