P>This study establishes a series of “indirect” identification frameworks to estimate the bridge frequencies by processing the dynamic response of traversing vehicles. Specifically, this study applies general system identification techniques, namely subspace identification, to the vehicle-bridge interaction (VBI) problem.

Firstly, the study verifies the stochastic subspace identification (SSI) with a realistic VBI model and discusses the effect of the road surface conditions (RRCs), vehicle speed, and regular on-going traffic. Numerical experiments show that the SSI method can effectively identify bridge frequencies when RRC is good. The on-going traffic can amplify the bridge vibration and facilitate the identification of higher modes of the bridge. However, the SSI method only allows lo...[ Read more ]

This study establishes a series of “indirect” identification frameworks to estimate the bridge frequencies by processing the dynamic response of traversing vehicles. Specifically, this study applies general system identification techniques, namely subspace identification, to the vehicle-bridge interaction (VBI) problem.

Firstly, the study verifies the stochastic subspace identification (SSI) with a realistic VBI model and discusses the effect of the road surface conditions (RRCs), vehicle speed, and regular on-going traffic. Numerical experiments show that the SSI method can effectively identify bridge frequencies when RRC is good. The on-going traffic can amplify the bridge vibration and facilitate the identification of higher modes of the bridge. However, the SSI method only allows low vehicle speed (about 1.39m/s=5km/h) that conceals the time-varying nature of the VBI system.

Then, the study proposes a short-time stochastic subspace identification (ST-SSI) method. To this end, it formulates the VBI problem using a dimensionless description, simplifying the VBI problem, and bringing forward the minimum number of parameters required for the identification. With the aid of the dimensionless parameters, the analysis manages to successfully apply ST-SSI despite the time-varying nature of the VBI system. The study eliminates the adverse effect due to the road surface roughness using a transformed residual vehicle response obtained from two vehicle traverses over the bridge. Numerical experiments are conducted to verify the ST-SSI method to identify a simply supported bridge using both a sprung mass model and a more realistic multi-degree-of-freedom (MDOF) vehicle model. The ST-SSI method succeeds in identifying the first two bridge frequencies for much higher vehicle speeds (e.g., 10m/s = 36km/h and 20m/s = 72km/h) than the SSI method, even in the presence of high levels of road surface roughness (adverse RRCs). However, the ST-SSI method limits the dimensionless speed of the vehicle to S_v1⩽0.1. The limitation becomes pronounced for identifying a continuous bridge with a short characteristic length of each span using a high-speed traversing vehicle. The concept of “short-time” cannot ignore the time-variation of the VBI system if the dimensionless speed of the traversing vehicle is high.

To this end, this study then establishes a multivariable output error state space (MOESP) scheme to estimate bridge frequencies considering the high dimensionless vehicle speeds. As a reference, for cases that the dimensionless speed is small (e.g., S_v1⩽0.1), the study exploits the concept of “short-time” to conceal the time-variation of the VBI system (i.e., the ST-MOESP method). If the dimensionless speed is large (e.g., S_v1˃0.1), the study employs the singular-value-decomposition (SVD) based pseudo-inverse algorithm on dealing with the time-varying nature of the VBI system (i.e., the MOESP method). This study investigates both the ST-MOESP and MOESP methods to identify a simply supported bridge and a 3-span bridge. The numerical experiments concern both a sprung mass vehicle and a more realistic MDOF vehicle. Numerical experiments demonstrate that proposed approaches succeed in identifying structural frequencies of the bridge even under the assumption of high vehicle speeds and high levels of road surface roughness.

Finally, this study applies subspace identification methods, including the ST-SSI and MOESP approaches, to the response of a two-axle vehicle. According to the vehicle speed, the study applies the ST-SSI method to the processed vehicle response if the dimensionless vehicle speed is small (i.e., S_v1⩽0.1), while adopts the MOESP algorithm if S_v1>0.1. The processed vehicle response from two wheels considering a time shift completely eliminates the adverse effect due to the road surface roughness, without controlling any parameters of the vehicle to reduce the noise during the identification procedure. Results show that the proposed approaches show good performance in identifying bridge frequencies even considering high vehicle speeds, high levels of road surface roughness, and random traffic flow on the bridge.