使用AML和霍夫曼和园丁技术预测基岩纤维钢筋混凝土与飞灰混合的强度
Kennedy C Onyelowe1,2, Viroon Kamchoom3, Shadi Hanandeh4
1Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria. kennedychibuzor@kiu.ac.ug.
Scientific reports
|April 9, 2025
概括
机器学习准确地预测了使用飞灰的玄武纤维增强混凝土强度. 像XGBoosting和AdaBoost这样的先进模型为传统混凝土测试提供了经济高效,快速的替代方案.
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.
- 计算科学 计算科学
背景情况:
- 评估混凝土机械性能的传统方法耗时且昂贵.
- 用飞增强的玄武纤维增强混凝土 (BFRC) 是一种可持续的替代材料.
- 机器学习 (ML) 为高效的财产预测提供了一个潜在的解决方案.
研究的目的:
- 开发和评估ML模型,用于预测BFRC的压力和分裂拉伸强度.
- 为了比较这项预测任务的七种不同的ML技术的性能.
- 通过使用灵敏度分析来确定影响BFRC强度的关键因素.
主要方法:
- 从文献中收集了121个关于BFRC属性的实验数据记录.
- 利用了11个关键因素作为ML模型的输入参数.
- 训练并验证了七个ML模型 (XGBoosting,AdaBoost,KNN,SVR,GEP,GMDHNN) 使用80%的训练和20%的验证数据分割.
- 雇员绩效评估指数 (R2,MAE) 和霍夫曼和园丁的灵敏度分析.
主要成果:
- XGBoosting和AdaBoost在预测压力 (Fc) 和分裂拉力 (Fsp) 强度方面表现出卓越的性能,达到高R2值 (0.98为Fc,0.96为Fsp).
- AdaBoost 显示出优异的概括能力,Fsp 的低验证MAE 为0.124 MPa.
- 灵敏度分析确定了影响混凝土强度的关键因素,有助于材料优化.
结论:
- 机器学习技术,特别是XGBoosting和AdaBoost,为预测BFRC机械性能提供了准确和高效的方法.
- 这些ML方法显著减少了对广泛实验测试的需求,从而节省了成本和时间.
- 这些发现支持使用ML来优化BFRC配方以实现可持续建筑实践.
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