摘要
钢材在低温条件下,其主要力学性能指标与常温条件下相比将发生变化,本文以常用的结构钢材(Q235、16Mn、15MnV、16Mnq、14MnNbq)在低温下的强度试验数据为研究对象,应用灰色相关理论建立模型进行预测研究。由于灰色模型的指数特性和积分定义,本文提出了基于背景值优化的改进GM(1.1)模型进行低温下结构钢材强度的预测,并通过试验对所建模型的准确性与有效性进行验证。试验表明,用积分构造背景值的方法可以提高GM(1.1)模型的拟合精度和预测精度,其改进GM(1.1)模型的数据拟合精度高达97.5%,建模结果表明了该模型的有效性,拓宽了灰色理论在土木工程领域的应用,具有很好的工程实用价值。
The main mechanical properties of structural steel at low temperature would change compared with the normal temperature conditions. Taking the strength test data＇ at low temperature of commonly used steel （ Q235, 16Mn,15MnV,16Mnq,14MnNbq） as the research object, grey theory is used to establish a model to predict the structural steel strength. Due to the index characteristic and integral feature ofgrey model, improvedGM （ 1.1 ） model based on background value optimization is Proposed, which is verified by experiment. The results show that the improved GM（1.1） model is very effective to predict the strength of structural steel at low temperature, with the accuracy of 97.5%. And practical applications widen the range of grey theoryapplication in civil engineering.
出处
《建筑科学》
北大核心
2014年第1期7-11,共5页
Building Science
基金
国家自然科学基金项目(No.51178244)
关键词
灰色理论
低温
结构钢材
强度试验
GM(1
1)模型
背景值优化
grey theory
low temperature
structural steel
strength test
GM （ 1.1 ） model
background value optimization
