物理辅助的机器学习方法用于预测回收聚合混凝土混凝土的裂纹抗拉强度
Jianguo Liu1, Xiangyu Han1, Yin Pan1
1School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, People's Republic of China.
Scientific reports
|June 5, 2023
概括
预测回收聚合混凝土 (RAC) 中的抗裂性能对于其应用至关重要. 物理辅助的机器学习模型,特别是使用Firefly算法的AdaBoost,显示了RAC的优异预测性能.
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:
- 回收聚合混凝土 (RAC) 提供了环保优势,但在预测耐裂性方面面临挑战.
- 准确的抗裂预测对于扩大像RAC这样的可持续建筑材料的应用至关重要.
研究的目的:
- 开发精确的预测模型,用于RAC的分裂抗拉强度,这是抗裂的关键指标.
- 研究物理辅助机器学习 (ML) 方法在模拟RAC属性的有效性.
- 通过提高预测能力,加强回收聚合混凝土的应用.
主要方法:
- 在RAC中,利用裂变抗拉强度作为抗裂强度的主要衡量标准.
- 采用物理辅助机器学习 (ML) 方法来构建预测模型.
- 使用火算法优化了AdaBoost ML模型,以提高预测准确度.
主要成果:
- 通过Firefly算法增强的AdaBoost模型展示了RAC分裂拉伸强度的出色预测性能.
- 物理辅助方法在特征选择和模型验证方面被证明是有效的,大大提高了预测准确度.
- 该研究强调了物理原理在指导混凝土材料的ML模型开发中的重要性.
结论:
- 物理辅助的ML提供了一个强大的框架来预测回收聚合混凝土的抗裂性能.
- AdaBoost-Firefly算法显示出对模拟RAC属性的显著前景.
- 未来的研究应该专注于扩大数据集和探索适合小样本大小的算法,以提高模型通用性.
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