基于机器学习的钢筋混凝土梁的切削强度预测使用Levy飞行增强决策树
Aybike Özyüksel Çiftçioğlu1, Anıl Delikanlı1, Torkan Shafighfard2
1Department of Civil Engineering, Faculty of Engineering and Natural Sciences, Manisa Celal Bayar University, Manisa, Turkey.
Scientific reports
|July 28, 2025
概括
一个新的机器学习模型,Levy-DT,准确地预测了钢筋混凝土 (RC) T梁的剪切强度. 这种方法结合了决策树和利率飞行优化,优于可靠结构分析的其他方法.
科学领域:
- 结构工程 结构工程
- 机器学习 机器学习
- 计算力学 计算力学 计算力学
背景情况:
- 钢筋混凝土 (RC) T梁是必不可少的结构元素,但预测它们的剪切强度是复杂的.
- 传统的实证方法往往无法捕捉结构设计变量中的非线性关系.
- 准确的剪切强度预测对于安全高效的结构设计至关重要.
研究的目的:
- 引入和评估一种新的机器学习模型,Levy-DT,用于在RCT梁中增强剪强度预测.
- 为了将Levy-DT的性能与已建立的回归模型进行比较.
- 用模型解释性技术识别影响剪切强度的关键因素.
主要方法:
- 通过将决策树算法与Levy飞行优化集成,开发了Levy-DT.
- 在195个经过实验测试的RCT束数据集上训练并验证了6个回归模型.
- 员工交叉验证,R2,RMSE和MAE指标,以进行可靠的绩效评估.
- 对模型可解释性进行了SHAP分析.
主要成果:
- 与所有其他评估模型相比,Levy-DT表现出卓越的预测准确性和概括能力.
- 优化决策树,随机森林,AdaBoost,K-最近邻居和回归被用作基准.
- SHAP分析确定轴力和钢筋深度是剪切强度的重要预测因素.
结论:
- 拟议的Levy-DT模型提供了一种可靠和准确的方法来预测RCT光束的剪切强度.
- 将优化技术与机器学习相结合,可以提高结构工程中的预测性能.
- 这些发现为钢筋混凝土结构的设计和分析提供了宝贵的见解.
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