More Like this

1. Identification of Switched Autoregressive Systems from Large Noisy Data Sets

   https://doi.org/10.23919/ACC.2019.8814621  

   Hojjatinia, Sarah ; Lagoa, Constantino M. ; Dabbene, Fabrizio ( July 2019 , 2019 American Control Conference (ACC))
2. Identification of Markov Jump Autoregressive Processes from Large Noisy Data Sets

   Hojjatinia, Sarah ; Lagoa, Constantino ( August 2020 , 2020 IAFC World Congress)
3. Identification of Markov Jump Autoregressive Processes from Large Noisy Data Sets

   Hojjatinia, S. ; Lagoa, Constantino ( August 2020 , 2020 IFAC World Congress)
4. Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages

   https://doi.org/10.1080/01621459.2021.1942013  

   Wilms, Ines ; Basu, Sumanta ; Bien, Jacob ; Matteson, David S. ( August 2021 , Journal of the American Statistical Association)
5. Seismic fragility analysis using nonlinear autoregressive neural networks with exogenous input

   https://doi.org/10.1080/15732479.2021.1894184  

   Sheikh, Imran A. ; Khandel, Omid ; Soliman, Mohamed ; Haase, Jennifer S. ; Jaiswal, Priyank ( January 2021 , Structure and Infrastructure Engineering)

   null (Ed.)

   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. 

   more » « less