Stochastic Sub-space Identification Methods for Bridges

DeBrunner, V.; DeBrunner, L.S.; Wang, P.; Baldwin, J.D.C.; Medda, Alessio; Thai, Ha Thi Thu

doi:10.1109/acssc.2005.1599941

Cited by 2 publications

(4 citation statements)

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“…A bridge system under a moving load can be modeled using the following input-output state space model. ( 1) ( ) (1) where x(k) is the state vector, u(k) is the vector of input forces of the moving load and y(k) is the vector of output measurements. The system is assumed to be stationary.…”

Section: System Modelingmentioning

confidence: 99%

“…Fig. 2 illustrates the stabilization diagram of the bridge-load simulation using stochastic subspace identification (output-only model) which was used in [1]. We also use the same set of data as in Fig.…”

Section: Bridge-load Simulationmentioning

confidence: 99%

“…However, we can compare our results with previous (imperfect) identification on this bridge. The impulse response method [7] was applied by using a dropped hammer on the Walnut Creek Bridge in 1996, and the stochastic subspace identification method was used in 2004 [1]. Fig.…”

Section: Walnut Creek Bridgementioning

confidence: 99%

“…Previously, the stochastic subspace identification algorithm was applied to identify bridges using an output-only model [1]. This algorithm assumes that the inputs are white processes, which is never the case.…”

Section: Introductionmentioning

confidence: 99%

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Deterministic - Stochastic Subspace Identification for Bridges

Thai

DeBrunner

et al. 2007

2007 IEEE/SP 14th Workshop on Statistical Signal Processing

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System identification for structural engineering has received significant attention in the last thirty years. With the ever increasing capacity of computing technology, system identification has been applied to important structures such as bridges and aircraft. In the case of bridges, the output can easily be measured by accelerometers. Considerable research in system identification on bridges has been done using output-only models. Of course, it is difficult to measure the inputs on an in-service bridge. In this paper, we see how the inputs can be estimated from the output measurements. We then use an input-output model to develop an improved system identification technique for identifying bridges. We show that the proposed method using the estimated inputs yields superior identification in a simulated case (i.e., where everything is controlled). We then use the method on the in-service Walnut Creek Bridge located on the north-bound lanes of U.S. Interstate I-35 between Dallas, TX and Oklahoma City, OK.

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Section: System Modelingmentioning

confidence: 99%

Section: Bridge-load Simulationmentioning

confidence: 99%

Section: Walnut Creek Bridgementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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