超高性能混凝土的数据驱动设计的前景和应用
Bryan K Aylas-Paredes1, Taihao Han2, Advaith Neithalath3
1Department of Materials Science and Engineering, Missouri University of Science and Technology, 248A V. H. McNutt Hall, 1400 N. Bishop, Rolla, MO, 65409, USA.
Scientific reports
|March 19, 2025
概括
机器学习模型通过识别关键材料因素,准确地预测超高性能混凝土 (UHPC) 的压力强度. 这优化了UHPC设计,并减少了对增强材料开发的实验需求.
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:
- 超高性能混凝土 (UHPC) 为基础设施提供卓越的机械性能和耐久性.
- 诸如低水与粘合剂比率,SCM和纤维增强等因素显著影响UHPC性能.
- 机器学习 (ML) 越来越多地用于预测UHPC性能和优化混合物设计.
研究的目的:
- 审查ML应用在预测UHPC可操作性,机械和热性能方面.
- 探索未来的研究方向,包括数据交叉,生成AI和物理引导的ML.
- 开发和评估ML模型,以使用大量数据集预测UHPC压力强度.
主要方法:
- 一个全面的文献审查的ML应用在UHPC.
- 使用1300个UHPC记录的数据库开发ML模型.
- 应用夏普利添加式解释 (SHAP) 来评估变量的重要性.
主要成果:
- ML模型在预测UHPC压力强度方面表现出很高的准确性.
- SHAP分析表明,化学成分对研究材料的强度预测有很小的影响.
- 排除微不足道的变量提高了模型效率和预测准确度.
结论:
- 优化的ML模型可以显著提高UHPC材料设计和性能预测.
- 通过从ML模型获得数据驱动的洞察力,可以减少实验工作量.
- 将数据集扩展到更多样化的数据可以进一步提高模型的概括性和预测能力.
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