A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Parametric system identification of large‐scale structure using decoupled synchronized signals
Large‐scale structures are subjected to environmental loads or frequent seismic motion with irremediable effect. These loads have often multidirectional actions on structures, and it couples their responses and leads to multiple‐input multiple‐output (MIMO) problem. The complexity of MIMO model and the relative time‐delays in sensing networks are among major sources of error in dynamic properties identification in large scale structures. This study proposed a parametric‐time domain method to reduce the negative effect of these problems. For this purpose, the contribution of each input in the output signals is determined using QR decomposition and converts a MIMO problem into multiple single‐input multiple‐output (SIMO) ones. In this regard, an Autoregressive Moving Average with eXogenous (ARMAX) model is implemented on decoupled signals for modal identification. Further, for time synchronization of records, a cross‐correlation function has been used to achieve more precise results. The method was employed real strong‐motion response recorded by different sensors at a high rise 64‐story concrete building. Results demonstrate the promising precision of the proposed algorithm for identifying current structural modal properties under real earthquake excitations. Hence, structures can be monitored efficiently along seismic experiences to detect any possible variations in their structural features. The comparison between the output of the proposed method and previous study indicates a considerable improvement on accuracy of the estimated model property particularly on mode shapes.
Parametric system identification of large‐scale structure using decoupled synchronized signals
Large‐scale structures are subjected to environmental loads or frequent seismic motion with irremediable effect. These loads have often multidirectional actions on structures, and it couples their responses and leads to multiple‐input multiple‐output (MIMO) problem. The complexity of MIMO model and the relative time‐delays in sensing networks are among major sources of error in dynamic properties identification in large scale structures. This study proposed a parametric‐time domain method to reduce the negative effect of these problems. For this purpose, the contribution of each input in the output signals is determined using QR decomposition and converts a MIMO problem into multiple single‐input multiple‐output (SIMO) ones. In this regard, an Autoregressive Moving Average with eXogenous (ARMAX) model is implemented on decoupled signals for modal identification. Further, for time synchronization of records, a cross‐correlation function has been used to achieve more precise results. The method was employed real strong‐motion response recorded by different sensors at a high rise 64‐story concrete building. Results demonstrate the promising precision of the proposed algorithm for identifying current structural modal properties under real earthquake excitations. Hence, structures can be monitored efficiently along seismic experiences to detect any possible variations in their structural features. The comparison between the output of the proposed method and previous study indicates a considerable improvement on accuracy of the estimated model property particularly on mode shapes.
Parametric system identification of large‐scale structure using decoupled synchronized signals
Kord, Sadeq (author) / Taghikhany, Touraj (author)
2022-04-10
19 pages
Article (Journal)
Electronic Resource
English
Parametric time‐domain identification of multiple‐input systems using decoupled output signals
| Online Contents | 2014
A Distributed, Scalable, and Synchronized Framework for Large-Scale Microscopic Traffic Simulation
| British Library Conference Proceedings | 2005