机器学习模型优化与optuna用于准确预测预压混凝土梁的强度和裂纹行为
Yuanyong Wen1, Rongxin Guo2,3,4, Zhihong Duan1
1Yunnan Xuanhui Expressway Co., Ltd, Qujing, 655000, China.
Scientific reports
|January 20, 2026
概括
使用Optuna优化机器学习模型显著提高预压混凝土梁性能的预测. 这种方法提高了XGBoost,决策树,随机森林和LightGBM的准确性并降低了计算成本.
科学领域:
- 土木工程 土木工程是指土木工程.
- 结构工程 结构工程
- 计算科学 计算科学
背景情况:
- 预应力混凝土梁对于桥梁和建筑物的结构完整性至关重要.
- 准确预测这些结构的性能对于安全性和耐用性至关重要.
- 机器学习 (ML) 提供了性能预测的潜力,但超参数优化仍然是一个挑战.
研究的目的:
- 调查Optuna在预应力混凝土梁ML模型的超参数优化中的有效性.
- 优化后比较XGBoost,决策树 (DT),随机森林 (RF) 和LightGBM (LGBM) 的性能.
- 评估超参数调整对预测效率和准确性的影响.
主要方法:
- 应用Optuna,一个自动化的超参数优化框架.
- 调整了四个ML模型:XGBoost,DT,RF和LGBM.
- 通过使用性能指标验证优化模型,如强度和抗裂的R平方.
主要成果:
- 对于所有测试的ML模型,Optuna成功地确定了最佳的超参数组合.
- 轻GBM (LGBM) 模型实现了高精度,强度为R2>0.98,抗裂度为R2>0.8.
- 超参数优化显著提高了预测效率,减少了计算时间.
结论:
- 奥普图纳是优化ML超参数在结构工程应用中的有效工具.
- 优化的ML模型,特别是LGBM,为预压混凝土梁的性能提供了准确的预测.
- 这种方法可以提高预测准确度,同时最大限度地减少资源支出.
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