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Application of meta-model based parameter identification of a seismically retrofitted reinforced concrete building

机译:基于元模型的抗震加固钢筋混凝土建筑物参数识别的应用

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FE models for complex or large-scaled structures that need detailed modeling of structural components are usually constructed using commercial analysis softwares. Updating of such FE model by conventional sensitivity-based methods is difficult since repeated computation for perturbed parameters and manual calculations are needed to obtain sensitivity matrix in each iteration. In this study, an FE model updating procedure avoiding such difficulties by using response surface (RS) method and a Pareto-based multiobjective optimization (MOO) was formulated and applied to FE models constructed with a commercial analysis package. The test building is a low-rise reinforced concrete building that has been seismically retrofitted. Dynamic properties of the building were extracted from vibration tests performed before and after the seismic retrofits, respectively. The elastic modulus of concrete and masonry, and spring constants for the expansion joint were updated. Two RS functions representing the errors in the natural frequencies and mode shape, respectively, were obtained and used as the objective functions for MOO. Among the Pareto solutions, the best compromise solution was determined using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) procedure. A similar task was performed for retrofitted building by taking the updating parameters as the stiffness of modified or added members. Obtained parameters of the existing building were reasonably comparable with the current code provisions. However, the stiffiess of added concrete shear walls and steel section jacketed members were considerably lower than expectation. Such low values are seemingly because the bond between new and existing concrete was not as good as the monolithically casted members, even though they were connected by the anchoring bars.
机译:通常需要使用商业分析软件来构建需要对结构部件进行详细建模的复杂或大型结构的有限元模型。通过常规的基于灵敏度的方法来更新这种有限元模型是困难的,因为需要对受扰动的参数进行重复计算并需要进行手动计算才能在每次迭代中获得灵敏度矩阵。在这项研究中,通过使用响应面(RS)方法和基于Pareto的多目标优化(MOO)避免了此类困难的有限元模型更新程序被制定并应用于使用商业分析包构建的有限元模型。测试建筑物是经过抗震加固的低层钢筋混凝土建筑。分别从地震改造前后的振动测试中提取了建筑物的动态特性。更新了混凝土和砌体的弹性模量以及伸缩缝的弹簧常数。获得了分别代表固有频率和模式形状误差的两个RS函数,并将其用作MOO的目标函数。在Pareto解决方案中,最佳折衷解决方案是使用TOPSIS(与理想解决方案相似的优先顺序技术)确定的。通过将更新参数作为修改或添加的构件的刚度,对翻新建筑物执行了类似的任务。现有建筑物获得的参数可以合理地与当前法规规定相比较。但是,增加的混凝土剪力墙和钢制截面护套的刚度大大低于预期。如此低的值似乎是因为新旧混凝土之间的结合不如整体浇铸的构件好,即使它们通过锚固杆连接。

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