用玄武岩纤维增强的混凝土的压力强度行为建模
Kennedy C Onyelowe1,2, Ahmed M Ebid3, Shadi Hanandeh4
1Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria. kennedychibuzor@kiu.ac.ug.
Scientific reports
|April 3, 2025
概括
机器学习模型准确地预测了基岩纤维增强混凝土 (BFRC) 的压力强度. KNN,ANN和SVR模型实现了高精度,KNN在优化可持续建筑材料方面表现出卓越的性能.
科学领域:
- 材料科学与工程 材料科学与工程
- 土木工程 土木工程是指土木工程.
- 计算科学 计算科学
背景情况:
- 基岩纤维增强混凝土 (BFRC) 提供了增强的机械性能和耐用性.
- 玄武岩纤维是环保的,具有成本效益,并来自天然火山岩,与可持续发展目标保持一致.
- BFRC为现代建筑提供了可持续的解决方案,提高了性能并减少了对环境的影响.
研究的目的:
- 调查和优化基岩纤维增强混凝土 (BFRC) 压力强度的预测.
- 评估各种机器学习模型的有效性,以预测BFRC压力强度.
- 确定最有效的模型,以提高BFRC在可持续基础设施中的实际应用.
主要方法:
- 从BFRC压力强度的文献中收集了309个数据记录,包括混合物比例,年龄和纤维尺寸.
- 使用的机器学习模型:人工神经网络 (ANN),k-最近邻居 (KNN),支持矢量机器 (SVM),决策树和随机森林 (RF).
- 使用色数据挖掘软件 (版本3.36),用于模型开发和分析,包括泰勒图表和灵敏度分析.
主要成果:
- 所有开发的模型都显示出出色的预测准确性,达到约95%的准确性.
- 在ANN,KN和SVR模型中,高R2值为0.98.98.
- KNN的表现优于ANN和SVR,平均绝对误差 (MAE) 最低1.4MPa,平均平方误差 (MSE) 最低2.5MPa.
结论:
- 机器学习模型,特别是KNN,ANN和SVR,对于预测BFRC压力强度非常有效.
- 该研究验证了BFRC作为可持续和高性能建筑材料的潜力.
- 通过ML进行优化预测可以提高BFRC在基础设施开发中的实际应用和采用.
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