Search

In this paper, the problem of bridge health monitoring is formulated as an unsupervised semantic segmentation where the aim is to identify the onset of structural damage by detecting unexpected irregularities in the time series data. In particular, the main objective of this study is to approach this problem through an indirect sensing framework based on the concept of vehicle-mounted scanning. Thus, the sole source of information is the acceleration responses of the moving vehicle passing over the bridge. This is achieved through automatic feature learning by adopting the concept of matrix profile to conduct an all-pair-similarity search for a given time series, where the location of the contextual change is determined by the corresponding Corrected Arc Crossings (CAC) profile. The performance of the method is first evaluated through numerical investigations for different damage cases where the effect of operational uncertainties such as road roughness, and variability in vehicle properties are considered. Further, the method is experimentally evaluated using a lab-scale bridge model with different damage cases. Through these investigations, it is consistently observed that the location of the change point always coincides with the minimum point of the CAC profile. The proposed framework has three major advantages making it more appealing compared to the competing techniques. First, it has been fully formulated in the context of unsupervised learning where no access to labeled data is required. Second, it is capable of working with a limited amount of data yet providing promising results. Finally, it can be easily applied to online settings where data is streaming in real-time. These successful results demonstrate the potential of the proposed method as a cost-effective and rigorous bridge inspection tool and open up opportunities in the field of indirect bridge health monitoring. [ABSTRACT FROM AUTHOR]

In recent years, researchers have extensively explored the application of drive-by inspection technology for bridge damage assessment. This approach involves using the response of a sensing vehicle to identify damage. However, many existing methods rely on data collected from both healthy and damaged bridge conditions, which may not always be available. Therefore, this study introduces a fully unsupervised computer vision-based methodology for bridge structural health monitoring (BSHM) using drive-by inspection. It analyzes the time–frequency domain of a two-axle vehicle's response by deriving a novel formulation for the contact point response from vehicle axles. The axles signals are then processed through subtraction, filtering, and decomposition using empirical Fourier decomposition with an improved segmentation approach based on the Savitzky–Golay filter (SGEFD). Relevant Intrinsic Mode Functions (IMF) are extracted as features representing damage, and the Wavelet Synchro-squeezed transform (WSST) is obtained from these features and used as input for the damage assessment algorithm. The performance of two state-of-the-art unsupervised generative machine learning methods, namely convolutional variational autoencoders (CVAE) and convolutional adversarial autoencoders (CAAE), is compared for the damage assessment task. These methods are trained solely with the residual WSST obtained from the vehicle responses when traversing a bridge in its reference state. A damage index (DI) is defined based on the measured error between the original and reconstructed images, and a damage threshold is calculated from the DI distribution of samples from the benchmark bridge state. During testing, the error between the original and reconstructed WSST is compared to the damage threshold, enabling the classification of new samples as healthy or damaged. The methodology is evaluated using both numerical and experimental vehicle–bridge interaction (VBI) models, considering various damage locations, severities, and the influence of road roughness. [ABSTRACT FROM AUTHOR]

Developing robust bridge health monitoring (BHM) frameworks based on the vehicle-mounted sensing, or so-called indirect structural health monitoring (SHM) or Drive-by Bridge Inspection is currently a rapidly growing research area. The goal is to monitor any change in the structural integrity of a bridge using the vibration responses measured from sensors installed on a vehicle passing over the bridge. To this aim, we present a novel data-driven approach by integrating a nonlinear dimensionality-reduction technique using Uniform Manifold Approximation and Projection (UMAP) together with a non-parametric clustering technique using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Ultimately, through the combined use of UMAP and HDBSCAN, it is demonstrated that such methods align perfectly well with the problems and restrictions present when analyzing drive-by bridge inspection data. That is, in the drive-by bridge inspection context data sets may have low cardinality, and the individual clusters may have different sizes and densities and do not need to have a spherical shape. Furthermore, new clusters may emerge over time as new damage cases evolve. The pairing of UMAP and HDBSCAN will be shown to assist in these issues in a principled manner. For the validation, we first make use of the theoretical formulation of the vehicle-bridge interaction system and conduct a finite element simulation of this system to obtain the moving vehicle response once traveling over five different bridge states. Further, a simply supported bridge model in the laboratory is considered for the experimental validation. The structure of the bridge model is gradually changed by adding multiple stiffener plates, and by gradually increasing a concentrated mass at mid-span. The proposed method is then applied to identify changes that occur in the laboratory bridge model. The experimental results for damage characterization demonstrate that various bridge states can successfully be separated from one another. The contributions of the work are three-fold; (1) First, a novel feature extraction and classification framework based on the combined use of UMAP and HDBSCAN is developed and validated. (2) Second, this paper demonstrates one of the early successful attempts of drive-by bridge inspection for monitoring the progressive change in the structure of a bridge based on the extensive numerical and experimental investigations. (3) Finally, the research presented in this work can open up new opportunities for condition monitoring of bridge network. • A novel feature extraction and classification framework is developed and validated. • Superior performance of the method by combined use of UMAP and HDBSCAN is presented. • One of the early successful attempts of drive-by bridge inspection is demonstrated. [ABSTRACT FROM AUTHOR]