在预测FRP-混凝土界面键强度时,应用XGBoost模型,通过多算法合集进行优化
Yuxin Chen1, Yulin Zhang1, Chuanqi Li1
1School of Resources and Safety Engineering, Central South University, Changsha 410083, China.
Materials (Basel, Switzerland)
|June 27, 2025
概括
本研究介绍了一种先进的XGBoost模型,该模型使用Nevergrad进行了优化,以预测纤维增强聚合物 (FRP) -混凝土的结合强度. 新模型显著提高了预测准确性,并提供了对有影响力的设计因素的关键见解.
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:
- 精确预测纤维增强聚合物 (FRP) -混凝土界面键强度对于加强结构的安全性和寿命至关重要.
- 传统的经验模型往往缺乏复杂的结构设计所需的准确性.
- 开发强大的预测工具对于推进FRP在土木工程中的应用至关重要.
研究的目的:
- 开发一个高度准确和可解释的FRP混凝土界面键强度的预测模型.
- 通过先进的机器学习技术,克服现有实证方法的局限性.
- 确定控制FRP-混凝土结合强度的关键参数.
主要方法:
- 使用极端梯度提升 (XGBoost) 机器学习算法.
- 通过使用Nevergrad框架与七个集成优化器进行全球超参数优化,增强了XGBoost模型性能.
- 采用五倍交叉验证策略来实现强大的模型通用化.
- 通过使用855个单环剪切试验数据集验证模型.
主要成果:
- 在测试组中,优化的XGBoost模型在R2 = 0.9726,RMSE = 1.8745和MAE = 1.3857的测试组中取得了卓越的预测性能.
- 与现有的最佳实证模型相比,R2有22.3%的改善,RMSE (63.4%) 和MAE (61.8%) 的显著减少.
- SHAP的解释性分析确定了FRP的宽度,厚度,弹性模量和结合长度是影响结合强度的关键因素.
结论:
- 开发的XGBoost-Nevergrad模型在预测FRP-混凝土界面键强度方面取得了重大进展.
- 该模型为工程师提供了一个可靠和可解释的工具,增强了FRP增强结构的设计过程.
- 这种智能方法将高预测准确度与对材料行为和设计参数的宝贵见解相结合.
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