使用先进的机器学习评估工业废物混凝土增强钢纤维的强度
Kennedy C Onyelowe1,2, Viroon Kamchoom3, Ahmed M Ebid4
1Department of Civil Engineering, College of Engineering and Engineering Technology, Michael Okpara University of Agriculture, Umudike, Nigeria. kennedychibuzor@kiu.ac.ug.
Scientific reports
|March 8, 2025
概括
机器学习 (ML) 为评估混凝土的压力强度提供了一种可持续的替代方案,特别是在工业废物和钢纤维方面. 在本研究中,Kstar和决策表模型在准确和环保的结果方面被证明是最有效的.
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:
- 传统的压力强度测试是资源密集型和耗时的.
- 工业废物和钢纤维越来越多地用于混凝土,需要有效的评估方法.
- 机器学习 (ML) 为材料属性评估提供了一种可持续且具有成本效益的替代方案.
研究的目的:
- 开发和比较ML模型,以预测包含工业废物的钢纤维增强混凝土的压力强度.
- 为此应用确定最准确,最有效的ML模型.
- 分析输入变量对混凝土压力强度的敏感性.
主要方法:
- 收集了166个钢纤维增强混凝土的数据记录,将其分为培训 (80%) 和验证 (20%) 集.
- 使用Weka软件应用了五种ML算法:Kstar,M5Rules,ElasticNet,XNV和DT. 使用Weka软件应用了五种ML算法:Kstar,M5Rules,ElasticNet,XNV和DT.
- 使用SSE,MAE,MSE,RMSE,R2,R,WI,NSE,KGE和SMAPE等指标评估模型性能.
主要成果:
- 克斯塔尔和决策表 (DT) 模型在预测压力强度方面表现出卓越的性能.
- 灵敏度分析显示,水 (W),细聚合物 (FAg),粗聚合物 (CAg) 和纤维体积分数 (Vf) 显著影响压力强度.
- 67%的纤维体积分数 (Vf) 和钢纤维方向 (61%) 被确定为抗裂和结构完整性的关键因素.
结论:
- ML提供了一种高效,成本效益和可持续的方法来评估工业废物混凝土的压力强度.
- 建议使用Kstar和DT模型,因为它们在混凝土物业评估中的精度和可持续性.
- 优化纤维含量和方向对于提高钢纤维钢筋混凝土的机械性能至关重要.
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