预测和可靠性分析RC深梁的剪切强度
1Department of Structural Engineering, Mansoura University, PO Box 35516, Mansoura, Egypt. k.megahed@mans.edu.eg.
Scientific reports
|June 25, 2024
概括
机器学习模型准确地预测了钢筋混凝土深梁切割强度,优于传统方法. CatBoost 展示了卓越的性能,为结构设计提供了可靠的替代方案.
科学领域:
- 结构工程 结构工程
- 计算力学 计算力学 计算力学
- 人工智能的人工智能
背景情况:
- 钢筋混凝土深梁 (RCDB) 是关键的结构元素.
- 准确预测它们的剪切强度对于安全和经济的设计至关重要.
- 传统方法往往缺乏足够的准确性或实用性.
研究的目的:
- 评估和比较八个机器学习 (ML) 模型的性能,以预测RCDB剪切强度.
- 开发基于ML模型的实际设计表达式.
- 将ML模型的可靠性与已建立的工程代码进行比较.
主要方法:
- 使用了一个包含840个RCDB样本和14个输入特征的综合数据库.
- 八个ML模型,包括CatBoost,符号回归等,经过训练和验证.
- 使用贝叶斯优化 (BO) 进行了超参数调整.
主要成果:
- CatBoost模型获得了最高的准确性 (R2 = 0.997训练,0.947测试).
- 符号回归产生了非常准确的实际设计表达 (平均. 的比率为0.935).
- 在准确性和可靠性方面,ML模型显著超过了传统的机械驱动模型和代码标准 (ACI318,EC2).
结论:
- 机器学习为预测RCDB剪切强度提供了一种高度可靠和准确的方法.
- 与传统的工程代码相比,ML模型显示出更高的性能.
- 该研究为结构工程师提供了经过验证的ML工具和设计见解.
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