使用梯度增强算法评估和估计混凝土造镜的压力强度
Lanh Si Ho1,2, Van Quan Tran1
1University of Transport Technology, Thanh Xuan, Hanoi, Vietnam.
PloS one
|March 5, 2024
概括
本研究提出了一个梯度增强模型,以准确预测空心混凝土造镜的压力强度 (CS). 机器学习方法为结构设计的传统实验室测试提供了更快,更具成本效益的替代方案.
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:
- 压力强度 (CS) 对于设计空心混凝土造结构至关重要.
- 传统的CS实验室测试耗时且昂贵.
- 机器学习为CS估计提供了一个有希望的替代方案.
研究的目的:
- 开发和验证一个梯度提升 (GB) 模型,用于预测空洞造镜的CS.
- 将GB模型的性能与其他机器学习技术进行比较.
- 确定影响空洞造镜的CS的关键参数.
主要方法:
- 利用了来自已发表文献的102个空心混凝土标本的数据库.
- 采用渐变增强 (GB) 与K折交叉验证和粒子群优化 (PSO) 进行超参数调整.
- 输入特征包括砂压力强度 (fm),块压力强度 (fb),高度对厚度比 (h/t) 和fm/fb比.
主要成果:
- 在GB模型实现了高预测准确度 (R2=0.977,RMSE=0.803 MPa,MAE=0.612 MPa,MAPE=0.036%).
- 在预测CS方面,GB超越了其他六种机器学习模型.
- 灵敏度分析显示fb和h/t是对CS影响最大的因素.
结论:
- 开发的GB模型提供了一个准确和有效的方法来评估空洞的造物质镜CS.
- 这种方法可以显著有利于在体结构设计中的实际应用.
- 该研究强调了机器学习在材料工程和结构分析方面的潜力.
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