使用基于深森林的模型预测混凝土的压力强度.
Wan Zhang1, Jiangtao Guo1, Cuiping Ning1
1College of Architecture Engineering, Yangling Vocational & Technical College, Shaanxi, Yangling, 712100, Shaanxi, China.
Scientific reports
|August 14, 2024
概括
这项研究比较了12个机器学习模型来预测混凝土的压力强度,发现Deepforest是最佳预测器. 这为施工质量控制提供了更快,更准确的方法.
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:
- 传统的混凝土压力强度测试是耗时和劳动密集的.
- 机器学习 (ML) 为预测混凝土强度提供了一个有希望的替代方案.
- 需要对ML模型进行全面比较,以确定最佳预测器.
研究的目的:
- 开发和比较12个不同的基于ML的回归器,用于预测混凝土的压力强度.
- 为准确的强度预测确定最佳的ML模型.
- 分析强度和关键组成因素之间的相关性.
主要方法:
- 开发了12ML的回归器,包括Deepforest.
- 选择的关键因素:高炉渣,超级可塑剂,年龄,水泥和水.
- 利用网格搜索和五倍交叉验证进行超参数调整.
主要成果:
- 基于Deepforest的模型表现优于其他11个ML模型.
- 在一个独立的测试数据集上实现了高性能指标 (0.91的R2).
- 与最先进的模型相比,证明了更高的准确性.
结论:
- 深森林模型是混凝土压力强度的最佳预测指标.
- 这种ML方法显著提高了施工质量控制的效率和准确性.
- 这些发现为在土木工程中实施先进的预测分析提供了坚实的基础.
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