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Parametric Bootstrap for System Identification of a Scaled Reinforced Concrete Bridge
Statistical analysis of the sensor network data is critical in processing the information and establishing frameworks for interpretation and comparison. Sensor networks in structural engineering applications produce small data sets in many cases, including during an event such as an earthquake when the duration of the event is limited to tens of seconds and hence the data sets are small. In the absence of abundant data, the bootstrap method is a statistical technique that can be used to generate the desired parameters. Additionally, compatibility between finite element models and the measured data is often used as a means to validate the model, the data, and the signal processing methods. In this paper the data from a scaled laboratory test of a reinforced concrete bridge specimen is used to demonstrate the effectiveness of both of these methods. A finite element model of the bridge, created in OpenSees, is calibrated such that the modal properties of the model match that of the data. Once the modal properties of the bridge are validated and a system identification model is selected that fits the data, the parametric bootstrap technique is used to generate statistical properties of the modes, including their confidence intervals.
Parametric Bootstrap for System Identification of a Scaled Reinforced Concrete Bridge
Statistical analysis of the sensor network data is critical in processing the information and establishing frameworks for interpretation and comparison. Sensor networks in structural engineering applications produce small data sets in many cases, including during an event such as an earthquake when the duration of the event is limited to tens of seconds and hence the data sets are small. In the absence of abundant data, the bootstrap method is a statistical technique that can be used to generate the desired parameters. Additionally, compatibility between finite element models and the measured data is often used as a means to validate the model, the data, and the signal processing methods. In this paper the data from a scaled laboratory test of a reinforced concrete bridge specimen is used to demonstrate the effectiveness of both of these methods. A finite element model of the bridge, created in OpenSees, is calibrated such that the modal properties of the model match that of the data. Once the modal properties of the bridge are validated and a system identification model is selected that fits the data, the parametric bootstrap technique is used to generate statistical properties of the modes, including their confidence intervals.
Parametric Bootstrap for System Identification of a Scaled Reinforced Concrete Bridge
Pakzad, Shamim N. (author) / Dryden, Matthew (author) / Fenves, Gregory L. (author)
Structures Congress 2009 ; 2009 ; Austin, Texas, United States
Structures Congress 2009 ; 1-9
2009-04-29
Conference paper
Electronic Resource
English
Parametric Bootstrap for System Identification of a Scaled Reinforced Concrete Bridge
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