Vehicle-Assisted Techniques for Health Monitoring of Bridges

Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges

Raw Data Is All You Need: Virtual Axle Detector with Enhanced Receptive Field

Rising maintenance costs of ageing infrastructure necessitate innovative monitoring techniques. This paper presents a new approach for axle detection, enabling real-time application of Bridge Weigh-In-Motion (BWIM) systems without dedicated axle detectors. The proposed method adapts the Virtual Axle Detector (VAD) model to handle raw acceleration data, which allows the receptive field to be increased. The proposed Virtual Axle Detector with Enhanced Receptive field (VADER) improves the $F_1$ score by 73\% and spatial accuracy by 39\%, while cutting computational and memory costs by 99\% compared to the state-of-the-art VAD. VADER reaches a $F_1$ score of 99.4\% and a spatial error of 4.13~cm when using a representative training set and functional sensors. We also introduce a novel receptive field (RF) rule for an object-size driven design of Convolutional Neural Network (CNN) architectures. Based on this rule, our results suggest that models using raw data could achieve better performance than those using spectrograms, offering a compelling reason to consider raw data as input

Wavelet-based operating deflection shapes for locating scour-related stiffness losses in multi-span bridges

Scour erosion poses a significant risk to bridge safety worldwide and remains among the top causes of failure. Scour at bridge foundations changes the stiffness of the soil-foundation system, resulting in global changes in the dynamic behavior of the bridge. In this paper, a new approach to detect the loss in foundation stiffness resulting from scour at multiple foundation locations is proposed, using wavelet-based Operating Deflection Shape (ODS) amplitudes. A numerical model of a bridge with four simply supported spans resting on piers is used to introduce and test the approach. Scour erosion is modelled as a reduction in vertical foundation stiffness under one or multiple bridge piers. A fleet of passing trucks, modelled as half-car vehicles, are used to excite the bridge to enable structural accelerations be calculated at an ‘accelerometer’ (sensor node) located at each support. The proposed method is shown to be effective with only one accelerometer at each support location in a multi-span bridge. Using a statistical population of passing vehicles, the temporal accelerations measured at each support are averaged and transformed into the frequency–spatial domain, in order to estimate the wavelet-based ODS for a given scour case. A damage indicator is postulated based on differences between the ODS of healthy and scoured bridge cases. The damage indicator enables visual identification of the location of scoured piers considering a range of natural frequencies of the system

Comparative studies of computation tools for moving force

Existing techniques to identify moving forces based on traditional finite element method (TFEM) is subject to a large number of elements with detailed description of a structure, which makes modeling complicated. A new modeling method for a vehicle-bridge system called wavelet finite element method (WFEM) is presented in this paper. It makes use of a multi-scale analysis whereby detailed description can be achieved to overcome this problem. The shape function of WFEM is formed by a scale function in a wavelet space and by a transformation matrix to connect the wavelet space to the physical one. To evaluate the properties of WFEM, simulations of two moving forces on a simply supported and a continuous bridge are conducted with subsequent comparison with TFEM. To smooth the noise and large fluctuations on the boundaries of the identified results in the time history, the first-order Tikhonov regularizations combined with the dynamic programming technique are adapted and compared with the zeroth-order Tikhonov regularization. White noise is added to the simulated dynamic responses. Some parameter effects, such as vehicle bridge parameters, measurement parameters are also considered. Numerical results demonstrate the WFEM method and the first-order Tikhonov regularization method to be effective for moving force identification. The first-order Tikhonov regularization has the property of smoothing noise and avoiding large fluctuations on the boundaries. Meanwhile, the parameters analyzed affect the identified results to some extent

Virtual Axle Detector Based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is a prerequisite in most cases. To avoid the use of conventional axle detectors and bridge type-specific methods, we propose a novel method for axle detection using accelerometers placed arbitrarily on a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This allows our method to use acceleration signals from any location on the bridge structure and act as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Results of the measurement data show that our model detects 95% of the axles, which means that 128,599 out of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles were detected with a maximum spatial error of 20 cm, at a maximum velocity of vmax=56.3m/s. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions

A Weigh-in-Motion Characterization Algorithm for Smart Pavements Based on Conductive Cementitious Materials

Smart materials are promising technologies for reducing the instrumentation cost required to continuously monitor road infrastructures, by transforming roadways into multifunctional elements capable of self-sensing. This study investigates a novel algorithm empowering smart pavements with weigh-in-motion (WIM) characterization capabilities. The application domain of interest is a cementitious-based smart pavement installed on a bridge over separate sections. Each section transduces axial strain provoked by the passage of a vehicle into a measurable change in electrical resistance arising from the piezoresistive effect of the smart material. The WIM characterization algorithm is as follows. First, basis signals from axles are generated from a finite element model of the structure equipped with the smart pavement and subjected to given vehicle loads. Second, the measured signal is matched by finding the number and weights of appropriate basis signals that would minimize the error between the numerical and measured signals, yielding information on the vehicle’s number of axles and weight per axle, therefore enabling vehicle classification capabilities. Third, the temporal correlation of the measured signals are compared across smart pavement sections to determine the vehicle weight. The proposed algorithm is validated numerically using three types of trucks defined by the Eurocodes. Results demonstrate the capability of the algorithm at conducting WIM characterization, even when two different trucks are driving in different directions across the same pavement sections. Then, a noise study is conducted, and the results conclude that a given smart pavement section operating with less than 5% noise on measurements could yield good WIM characterization results

Utilizing the system instantaneous frequency for the structural health monitoring of bridges

This thesis focuses on the time-frequency analysis of the vehicle-bridge dynamic interaction response to identify the time-dependent resonances of railway bridges which are incorporated into a damage detection approach, rather than only system identification. Most of the current system identification techniques applied to bridges are based on the free vibration response analysis. It is known that the bridge free vibration response is sensitive to environmental conditions such as temperature and it is not sufficiently sensitive to damage. Input-output modal analysis or output-only modal analysis are the other most used techniques for the bridge system identification. The train-bridge dynamic response, obtained during passage of the train is potentially more sensitive to damage, but also a more complex signal to analyze. First of all, it is a nonstationary signal that is not valid for modal analysis. In addition to the time-variant nature, the vehicle-bridge dynamic response can show closely-spaced spectral components response. These features disrupt the performance of the most advanced signal processing techniques. This thesis therefore applies a recently developed technique, Wavelet Synchrosqueezed Transform (WSST) to extract the Instantaneous Frequencies (IFs) of the Vehicle-Bridge Interaction (VBI) system response. A comparative study is performed on the various commonly used time-frequency analysis techniques. The obtained results were further validated using field measurements on a real bridge. Subsequently, a concept for damage detection in (railway) bridges based on the instantaneous frequency analysis of the bridge’s forced and free vibration responses is proposed. Within this concept, based on the bridge natural frequency extracted from the bridge free vibration, a healthy baseline is obtained of the bridge forced vibration response. The shape correlation and the magnitude variation are proposed to distinguish between the global characteristics of the bridge baseline induced by variable operational conditions and the local deviations caused by damage. of the baseline deviation is damage, then the magnitude variation can be used as a damage index. The results of the numerical studies show that trains with single suspension systems cause more pronounced changes in the bridge’s frequency response, specifically the Vehicle-Induced Delta Frequency (VIDF) and Damage-Induced Delta Frequency (DIDF), than dual suspension trains

A non-contact vision-based system for multi-point displacement monitoring in a cable-stayed footbridge

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Vision-based monitoring receives increased attention for measuring displacements of civil infrastructure such as towers and bridges. Currently, most field applications rely on artificial targets for video processing convenience, leading to high installation effort and focus on only single-point displacement measurement e.g. at mid-span of a bridge. This study proposes a low-cost and non-contact vision-based system for multi-point displacement measurement based on a consumer-grade camera for video acquisition and a custom-developed package for video processing. The system has been validated on a cable-stayed footbridge for deck deformation and cable vibration measurement under pedestrian loading. The analysis results indicate that the system provides valuable information about bridge deformation of the order of a few cm induced, in this application, by pedestrian passing. The measured data enables accurate estimation of modal frequencies of either the bridge deck or the bridge cables and could be used to investigate variations of modal frequencies under varying pedestrian loads