基于机器学习的预测UHPC强化钢筋混凝土柱的轴承负载能力:一个比较分析
Viet Hai Hoang1, Minh Quang Tran2, Van Thuc Ngo3
1Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam.
PloS one
|January 7, 2026
概括
机器学习模型准确地预测了用超高性能混凝土外套加强的钢筋混凝土柱的轴承负载能力. CatBoost 模型表现出卓越的性能,为结构增强设计提供了可靠的工具.
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
- 计算力学 计算力学 计算力学
背景情况:
- 钢筋混凝土 (RC) 柱是关键的结构元素.
- 用超高性能混凝土 (UHPC) 外套加强现有的RC柱子是一种常见的改装技术.
- 精确预测强化柱子的轴承负载能力对于结构安全和设计优化至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测用UHPC外套增强的RC柱的轴承负载能力 (Pu).
- 为ML模型培训和验证建立迄今为止最广泛的UHPC外套RC列数据集.
- 为了比较各种ML模型的性能,并确定这个预测任务最有效的模型.
主要方法:
- 编制一个全面的实验数据库,包括105个试验样本和17个输入参数.
- 开发和评估六个ML模型:极端随机树 (ER),K-最近邻居 (KNN),光梯度增强机 (LightGBM),Xgboost,CatBoost和级联前进神经网络 (CFNNs).
- 使用SHAP分析来分析模型的可解释性,并了解输入参数对预测轴负载能力的影响.
主要成果:
- CatBoost模型实现了最高的预测准确性,其中R2 = 0.983,MAE = 177 kN,RMSE = 211 kN.
- 开发的ML模型在预测轴承负载能力方面明显优于ACI 318和EC2等传统设计代码.
- SHAP 分析提供了有关 UHPC 套装 RC 柱的关键参数的宝贵见解.
结论:
- 先进的ML模型,特别是CatBoost,提供了一个非常准确和可靠的方法来预测UHPC外套式RC柱的轴承负载能力.
- 机器学习模型有效地捕捉了复杂的非线性行为,这些行为对传统的设计代码具有挑战性.
- 拟议的ML框架对工程师来说是一个有价值的工具,支持用于结构强化的更安全,更有效的设计策略.
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