使用机器学习预测混凝土的压力强度,其中包括暴露在高温下的废物粉末
Islam N Fathy1, Hany A Dahish2, Mohammed K Alkharisi3
1Construction and Building Engineering Department, October High Institute for Engineering & Technology, Giza, Egypt.
Scientific reports
|July 12, 2025
概括
这项研究使用机器学习来预测在加热下使用废大理石和花岩粉末的混凝土强度. XGBoost模型准确地预测了强度,帮助利用回收材料进行可持续建筑.
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:
- 建筑垃圾粉末为混凝土中的水泥提供了一个可持续的替代品.
- 高温显著降低了混凝土的机械性能.
- 预测模型对于理解废粉对混凝土强度的影响至关重要.
研究的目的:
- 开发和评估机器学习模型,以预测含有废旧大理石和花岩粉末的混凝土的压力强度.
- 为了比较极端梯度增强 (XGBoost),随机森林 (RF) 和M5P模型的性能.
- 确定影响混凝土在热应力下压力强度的关键因素.
主要方法:
- 使用了324个混凝土样本的数据集,其中含有不同的废花粉 (GWP) 和废大理石粉 (MWP) 含量,温度 (T) 和持续时间 (D).
- 开发了使用XGBoost,RF和M5P算法的预测模型.
- 优化的超参数与网格搜索和使用K折交叉验证,R2,MAPE,RMSE和MAE的验证模型.
主要成果:
- 该XGBoost模型实现了最高的准确性,R2为0.9989和最小的错误 (MAE:0.1351MPa,RMSE:0.1842MPa,MAPE:0.48%).
- 废粉添加物 (GWP,MWP) 对压力强度产生了积极的影响.
- 高温对压力强度产生了最显著的负面影响.
结论:
- XGBoost 是一个非常有效的工具,用于预测可持续混凝土在高温下的压力强度.
- 开发的模型和附带的GUI可以帮助研究人员和建筑行业优化混凝土混合设计.
- 纳入建筑废弃物粉末有望提高混凝土的性能,同时促进可持续性.
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