Abstract

Rapidly growing societal needs in urban areas are increasing the demand for tall buildings with complex structural systems. Many of these buildings are located in areas characterized by high seismicity. Quantifying the seismic resilience of these buildings requires comprehensive fragility assessment that integrates iterative nonlinear dynamic analysis (NDA). Under these circumstances, traditional finite element (FE) analysis may become impractical due to its high computational cost. Soft-computing methods can be applied in the domain of NDA to reduce the computational cost of seismic fragility analysis. This study presents a framework that employs nonlinear autoregressive neural networks with exogenous input (NARX) in fragility analysis of multi-story buildings. The framework uses structural health monitoring data to calibrate a nonlinear FE model. The model is employed to generate the training dataset for NARX neural networks with ground acceleration and displacement time histories as the input and output of the network, respectively. The trained NARX networks are then used to perform incremental dynamic analysis (IDA) for a suite of ground motions. Fragility analysis is next conducted based on the results of the IDA obtained from the trained NARX network. The framework is illustrated on a twelve-story reinforced concrete building located at Oklahoma State University, Stillwater campus.

摘要

城市地区快速增长的社会需求正在增加对具有复杂结构系统的高层建筑的需求。这些建筑物中的许多都位于以高地震活动为特征的地区。量化这些建筑物的抗震能力需要综合的脆弱性评估，该评估集成了迭代非线性动力分析 (NDA)。在这些情况下，传统的有限元 (FE) 分析由于其高计算成本而变得不切实际。软计算方法可以应用于 NDA 领域，以降低地震易损性分析的计算成本。本研究提出了一个框架，该框架在多层建筑的脆弱性分析中采用具有外生输入 (NARX) 的非线性自回归神经网络。该框架使用结构健康监测数据来校准非线性有限元模型。该模型用于生成 NARX 神经网络的训练数据集，其中地面加速度和位移时间历史分别作为网络的输入和输出。然后使用经过训练的 NARX 网络对一组地面运动执行增量动态分析 (IDA)。接下来基于从经过训练的 NARX 网络获得的 IDA 的结果进行脆弱性分析。该框架在位于俄克拉荷马州立大学斯蒂尔沃特校区的一座 12 层钢筋混凝土建筑上进行了说明。接下来基于从经过训练的 NARX 网络获得的 IDA 的结果进行脆弱性分析。该框架在位于俄克拉荷马州立大学斯蒂尔沃特校区的一座 12 层钢筋混凝土建筑上进行了说明。接下来基于从经过训练的 NARX 网络获得的 IDA 的结果进行脆弱性分析。该框架在位于俄克拉荷马州立大学斯蒂尔沃特校区的一座 12 层钢筋混凝土建筑上进行了说明。