对数字和机器学习方法进行全面的审查,以预测混凝土属性:从新鲜到长期
Nilam Adsul1, Yongho Choi2, Su-Tae Kang3
1Department of Civil Engineering, Daegu University, Gyeongsan 38453, Republic of Korea.
Materials (Basel, Switzerland)
|August 14, 2025
概括
与传统的数值方法相比,机器学习 (ML) 模型在预测具体属性方面提供了更高的准确性. 在水泥复合材料研究中,有效的数据预处理对于优化ML和数值模型的性能至关重要.
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.
- 计算建模 计算建模
背景情况:
- 由于材料多样性和创新的增加,水泥复合材料需要预测模型.
- 现有的数值,基于代码和机器学习 (ML) 模型预测具体的属性,但对输入变量敏感.
- 准确预测混凝土的特性需要模型,这些模型整合混合物设计,组成,内在特性和外部条件.
研究的目的:
- 对数值,基于代码和ML建模技术进行全面的审查,以预测混凝土属性.
- 评估数据收集,预处理和处理对模型性能的影响.
- 将ML模型的预测精度与传统的数值方法进行比较.
主要方法:
- 审查现有的关于具体属性的数值,基于代码和ML建模的文献.
- 分析影响模型准确性的因素,包括数据变化和数据集大小.
- 对各种ML算法和传统数值模型的性能指标进行比较.
主要成果:
- 机器学习模型表现出比传统的数值方法更优越的预测性能.
- 数据预处理对数值模型和机器学习模型的准确性产生重大影响.
- 基于ML的方法,包括组合和非组合模型,显示出强大的预测能力.
- 用额外参数修改的数值模型显示了更好的准确性.
- 优化算法和可解释性工具提高了模型的可靠性和透明度.
结论:
- 机器学习模型对于预测广泛的新鲜和长期混凝土性能非常有效.
- 强大的数据处理和预处理对于可靠的混凝土性能预测至关重要.
- 整合优化和可解释性工具对于推进具体建模至关重要.
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