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• An approach based on the BDLM and Markov-switching theory is presented for condition assessment of bridge expansion joints. • The EM and NR approaches are compared for estimating parameters of switching BDLM. • The temperature effect on the displacement is modeled by the superposition of harmonic components in BDLM. • The switching BDLM offers the transition probability from normal to degradation state of bridge expansion joints. Age-related deterioration and premature failure have been primary concerns for bridge expansion joints. It is essential to improve the understanding of their operational performance. The existing approaches mainly formulate the deterministic/probabilistic temperature-displacement relationship (TDR) model to assess the structural condition of bridge expansion joints. Nevertheless, it is not easy to guarantee a strong correlation between representative temperature and displacement. Rather than establishing the TDR model, this work uses the displacement response to evaluate the expansion joint condition by combining the Bayesian dynamic linear model (BDLM) with Markov-switching theory. The external temperature effect on the displacement is modeled by the superposition of harmonic components in BDLM. The expectation–maximization (EM) algorithm initialized by the subspace method is employed to optimize the initial parameters. The presented approach is validated through the simulated data, and then it is applied to the expansion joint of a long-span bridge. Results show that EM with the subspace method involves high computational accuracy and efficiency in estimating unknown parameters compared to the Newton-Raphson approach. The switching BDLM successfully identifies the degradation process of expansion joints and offers the transition probability from the normal to other states. [ABSTRACT FROM AUTHOR]

• This study is the first to propose end-to-end pipelines for comprehensive modal identification of a bridge using moving sensors within vehicles. • Both proposed methods are successful to remove vehicle suspension effects and roughness-induced vibrations from the collected data within the vehicle cabin in order to extract bridge dynamic response. • The study indicates that the FRF-based method is able to identify the bridge with high accuracy as well as an acceptable estimation of the roughness profile. • The EEMD-based method does not need any a priori information of the vehicle, leading a practically more flexible platform. • In a numerical 2D case study, first four modes are fully identified using the proposed pipelines. MAC values are all above 0.94. Natural frequencies and damping ratios are also estimated accurately for the majority of cases. Vehicles commuting over bridge structures respond dynamically to the bridge's vibrations. An acceleration signal collected within a moving vehicle contains a trace of the bridge's structural response, but also includes other sources such as the vehicle suspension system and surface roughness-induced vibrations. This paper introduces two general methods for the bridge system identification using data exclusively collected by a network of moving vehicles. The contributions of the vehicle suspension system are removed by deconvolving the vehicle response in frequency domain. The first approach utilizes the vehicle transfer function, and the second uses ensemble empirical modal decomposition (EEMD). Next, roughness-induced vibrations are extracted through a novel application of second-order blind identification (SOBI) method. After these two processes, the resulting signal is equivalent to the readings of mobile sensors that scan the bridge's dynamic response. Structural modal identification using mobile sensor data has been recently made possible with the extended structural modal identification using expectation maximization (STRIDEX) algorithm. The processed mobile sensor data is analyzed using STRIDEX to identify the modal properties of the bridge. The performance of the methods are validated on numerical case studies of a long single-span bridge with a network of moving vehicles collecting data while in motion. The analyses consider three road surface roughness patterns. Results show that for long-span bridges with medium- to high-ongoing traffic volume, the proposed algorithms are successful in extracting pure bridge vibrations, and produce accurate and comprehensive modal properties of the bridge. The study shows that the proposed transfer function method can efficiently deconvolve the linear dynamics of a moving vehicle. EEMD method is able to extract vehicle dynamic response without a priori information about the vehicle. In addition, proposed identification methods provide secondary information about the roughness pattern and the vehicle. This study is the first proposed methodology for complete bridge modal identification, including operational natural frequencies, mode shapes and damping ratios using moving vehicles sensor data. [ABSTRACT FROM AUTHOR]