基于机器学习的预测模型,用于3D打印混凝土的拉伸和屈曲强度
Ammar Ali1, Raja Dilawar Riaz1, Umair Jalil Malik1
1School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), H-12 Sector, Islamabad 44000, Pakistan.
Materials (Basel, Switzerland)
|June 10, 2023
概括
这项研究开发了机器学习模型来预测3D打印混凝土的屈曲和拉伸强度,大大减少了混合设计优化的实验努力.
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:
- 混凝土的增材制造 (3D打印混凝土) 提供了诸如降低成本和材料浪费等好处.
- 传统的混凝土建筑在优化混合设计方面面临挑战,原因是许多变量和广泛的实验.
- 现有的文献缺乏基于机器学习 (ML) 的预测模型,用于3D打印混凝土的机械性能.
研究的目的:
- 开发和评估3D打印混凝土的屈曲和拉伸强度的预测模型.
- 通过计算方法解决优化3D打印混凝土混合设计的挑战.
- 为了减少制定3D打印混凝土混合设计所需的实验和计算努力.
主要方法:
- 从25个关于3D打印混凝土性能的文献研究中收集了数据.
- 开发并比较了高斯过程回归,决策树回归,支持向量机 (SVM) 和XGBoost回归模型.
- 输入参数包括混合设计组件 (水,水泥,聚合物,添加剂,纤维) 和打印参数 (速度,喷嘴面积).
- 目标属性是曲和拉伸强度 (MPa).
主要成果:
- 支持矢量机 (SVM) 模型在基于R2,RMSE,MSE和MAE的其他模型相比,表现优越.
- 在所有模型中,造混凝土和印刷混凝土的相关屈曲强度值.
- 从数据集中对六种不同的混合比例的模型准确性进行了验证.
结论:
- 开发的ML模型,特别是SVM,可以准确地预测3D打印混凝土的屈曲和拉伸强度.
- 这项研究通过引入基于ML的预测模型来为3D打印的混凝土性能提供了一种新的方法.
- 这些模型有可能显著减少在增材制造中对混凝土混合物设计优化所需的时间和资源.
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