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首页> 外文期刊>Journal of Constructional Steel Research >Shear lag effect on stress concentration in simply-supported stiffened box girder
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Shear lag effect on stress concentration in simply-supported stiffened box girder

机译:简单地支持加强箱梁中应力集中的剪切滞后效应

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The shear lag effect on stress concentration in a simply-supported stiffened box girder is investigated by threedimensional finite element analysis. In the analysis, the dependence of the stress concentration on the finite element mesh is circumvented by the multimesh extrapolation method. An extensive parametric study is conducted with respect to the geometry of the stiffened box girder including the cross-sectional area of stiffeners. The influence of the method of load application is also studied. The stress concentration due to the shear lag is sensitive to the way a concentrated load is modeled, while relatively insensitive to the way a uniformly distributed load is modeled. The estimation of the stress concentration by the formula of Eurocode 3 agrees well with the present finite element analysis results for an unstiffened box girder. However, the formula tends to overestimate the influence of the stiffener. Regression analysis is then conducted, taking three approaches. The multiple linear regression equation is the simplest approach and helps identify the degree of the influence of each parameter, yet the accuracy is low, as the mean square error is as large as 16 & ndash;20%. Though a little more involved, the accuracy of the empirical formula is much higher, with the mean square error about 3%. An artificial intelligence technique, based on the regression model of the multi-layer perceptron, is also examined. The accuracy is the highest of the approaches tested: the mean square error is well below 1%.(c) 2021 Elsevier Ltd. All rights reserved.
机译:通过三重有限元分析研究了简单地支持的加强箱梁中应力浓度的剪切滞后效应。在分析中,通过多元素外推法避免了应力浓度对有限元啮合的依赖性。相对于加强箱梁的几何形状进行了广泛的参数研究,包括加强筋的横截面积。还研究了负荷应用方法的影响。由于剪切滞后引起的应力浓度对模型浓缩负荷的方式敏感,而对均匀分布的负载的方式相对不敏感。欧元码3公式的应力集中估计与不稳定的箱梁的目前的有限元分析结果吻合良好。然而,该公式倾向于高估加强件的影响。然后进行回归分析,采用三种方法。多个线性回归方程是最简单的方法,有助于识别每个参数的影响程度,但精度低,因为平均方误差大约16– 20%。虽然有点涉及,但经验公式的准确性要高得多,平均方误差约为3%。还研究了一种人工智能技术,基于多层Perceptron的回归模型。准确性是测试的最高方法:平均方误差远低于1%。(c)2021 elestvier有限公司保留所有权利。

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