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软计算模型用于预测新鲜混凝土的塑料粘度和接口屈服应力
Waleed Bin Inqiad1,2, Muhammad Faisal Javed3,4, Deema Mohammed Alsekait5
1School of Civil and Resources Engineering, University of Science and Technology, 17, Beijing, 100083, China.
Scientific reports
|March 29, 2025
概括
使用像XGBoost这样的机器学习模型预测混凝土的塑料粘度和接口产量应力可以节省时间和资源. 这些模型准确地估计了用于混凝土的关键性质,有助于土木工程应用.
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
- 计算科学 计算科学
背景情况:
- 新鲜混凝土的性能,特别是接口承应力和塑料粘度,对于其可性至关重要.
- 对这些属性的现场测试是耗时和资源密集的,需要使用替代方法.
研究的目的:
- 开发精确的预测模型,用于混凝土的塑性粘度和接口产应应力.
- 为了比较各种机器学习算法的性能和这个预测任务的统计技术.
主要方法:
- 使用随机森林回归 (RFR),基因表达编程 (GEP),K-最近邻居 (KNN),极端梯度增强 (XGB) 和多线性回归 (MLR).
- 模型使用已发表的文献数据进行训练,具有六个输入参数 (例如,水泥,水,混合后的时间) 和两个输出参数.
- 使用错误指标,k-fold验证和残余分析来评估性能. 在最佳模型上进行了SHAP和ICE分析.
主要成果:
- 极端梯度提升 (XGB) 在预测塑料粘度和接口屈服应力方面表现出最高的准确性.
- 基因表达编程 (GEP) 是唯一提供经验方程的算法.
- 水,水泥含量和混合后的时间被确定为使用XGB预测新鲜混凝土性能最有影响的参数.
结论:
- 机器学习,特别是XGBoost,提供了一种高度准确和高效的方法来预测新鲜混凝土的特性,减少了对现场广泛测试的需求.
- 开发了一个图形用户界面,以促进这些预测模型在土木工程行业的实际应用.
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