机器学习方法用于预测高强度混凝土的压力强度
Mohammed Shaaban1, Mohamed Amin2,3, S Selim4
1Civil Engineering Department, Faculty of Engineering, Delta University for Science and Technology, International Coastal Road, Gamasa, Egypt. Mohamed.selim@deltauniv.edu.eg.
Scientific reports
|July 15, 2025
概括
预测高强度混凝土 (HSC) 的压力强度至关重要. 使用Python的机器学习模型为准确的强度预测提供了传统实验室测试的经济有效和高效的替代方案.
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:
- 压力强度是高强度混凝土 (HSC) 的关键机械性质,对于结构安全至关重要.
- 传统的实验室方法来确定HSC压力强度是资源密集型的,涉及大量的时间和成本.
- 人工智能 (AI) 为开发高效准确的预测模型提供了一个有希望的途径.
研究的目的:
- 开发和评估一种机器学习 (ML) 模型,用于预测HSC的压力强度.
- 用Python研究各种回归模型在预测HSC压力强度方面的有效性.
- 展示AI在优化混凝土材料设计和性能评估方面的潜力.
主要方法:
- 利用来自HSC原始实验测试的数据集.
- 使用Python编程语言开发和实施ML模型.
- 研究了多重回归模型,优化了超参数,并使用MAE,MSE和R平方等指标评估性能.
主要成果:
- XGBoost 模型获得了高的 R 平方值,约为 0.94,这表明它具有更高的预测准确性.
- 超参数调整对于优化模型性能至关重要.
- 该研究成功地证明了ML模型能够准确预测HSC压力强度的能力.
结论:
- 机器学习模型,特别是XGBoost,显示出用于准确预测HSC压力强度的巨大潜力.
- Python 是一种适合于在土木工程应用中开发可靠的预测模型的编程语言.
- 人工智能驱动的方法可以大大减少与材料性质测试相关的时间和成本.
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