多任务学习在CFDST设计中的潜力:使用三个MTL模型进行负载能力设计
Zhenyu Wang1, Jian Zhou1, Kang Peng1
1School of Resources and Safety Engineering, Central South University, Changsha 410083, China.
Materials (Basel, Switzerland)
|May 11, 2024
概括
多任务学习模型准确预测混凝土填充双钢管 (CFDST) 的设计参数. 这种方法为CFDST结构设计和代码开发提供了新的见解,克服了现有的设计验证方法的局限性.
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:
- 用混凝土填充的双钢管 (CFDST) 通过复合材料的作用提高了性能.
- 目前的CFDST设计代码主要侧重于验证,缺乏直接参数提供.
- 多任务学习 (MTL) 为优化结构设计参数提供了一个有希望的方法.
研究的目的:
- 应用MTL技术来预测CFDST的多个设计参数.
- 在CFDST设计中评估不同MTL模型的准确性.
- 使用MTL. 探索CFDST设计参数之间的潜在关系.
主要方法:
- 利用现有文献中CFDST的227个单轴压缩测试案例.
- 应用了三个MTL模型:多任务拉索,VSTG和MLS-SVR.
- 使用R2,RMSE,RRMSE和 ρ统计指标评估模型准确性.
主要成果:
- 证明了CFDST设计参数之间的非线性关系.
- MLS-SVR提供了准确的设计参数预测.
- 多任务拉索和VSTG揭示了参数相互连接,VSTG将预测任务分组在一起.
结论:
- 对于CFDST设计参数预测,MTL,特别是MLS-SVR是非常有效的.
- MTL模型为CFDST参数之间的复杂关系提供了有价值的见解.
- 这项研究引入了CFDST设计的新方法,并为未来的代码开发提供了信息.
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