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With the increasing demand for sustainable infrastructure maintenance, it has become very important to predict the health condition of the structures in real-time. This study investigates the application of machine learning techniques for assessing the structural health of prototype beam bridges. By employing magnetostrictive sensors, which convert mechanical vibrations into electrical energy, the research aims to perform frequency analysis to predict dominant frequencies in a prototype beam bridge. Data were collected using a Digital Storage Oscilloscope and a Data Acquisition Card, followed by comprehensive feature extraction and dimensionality reduction. Machine learning models, including Random Forest and Deep Neural Networks, were utilised to classify waveform types and predict vibration frequencies. The Random Forest model achieved a classification accuracy of 86.16% and a mean absolute percentage error of 4.33% in frequency prediction, highlighting its superior accuracy and reliability for continuous bridge health monitoring. These results demonstrate the potential to revolutionise modern infrastructure maintenance practices by enabling real-time, automated assessments of structural integrity. [ABSTRACT FROM AUTHOR]

Featured Application: This work can be applied to estimate acceleration using measured inclination, withoutpartially measuring the target acceleration. Moreover, this is a finiteelement model-free method and can be conveniently applied to beam bridges. Data fusion is an important issue in bridge health monitoring. Through data fusion, specific unknown bridge responses can be estimated with measured responses. However, existing data fusion methods always require a precise finite element model of the bridge or partially measured target responses, which are hard to realize in actual engineering. In this study, we propose a novel data fusion method. Measured inclinations across multiple cross-sections of the target bridge and accelerations at a subset of these sections were used to estimate accelerations at the remaining sections. Theoretical analysis of a typical vehicle-bridge interaction (VBI) system has shown parallels with the blind source separation (BSS) problem. Based on this, Independent Component Analysis (ICA) was applied to derive surrogate inclination mode shapes. This was followed by calculating surrogate displacement mode shapes through numerical integration. Finally, a surrogate inter-section transfer matrix for both measured and unmeasured accelerations was constructed, enabling the estimation of the target accelerations. This paper presents three key principles involving the relationship between the surrogate and actual inter-section transfer matrices, the integration of mode shape functions, and the consistency of transfer matrices for low- and high-frequency responses, which form the basis of the proposed method. A series of numerical simulations and a large-scale laboratory experiment were proposed to validate the proposed method. Compared to existing approaches, our proposed method stands out as a purely data-driven technique, eliminating the need for finite element analysis assessment. By incorporating the ICA algorithm and surrogate mode shapes, this study addresses the challenges associated with obtaining accurate mode shape functions from low-frequency responses. Moreover, our method does not require partial measurements of the target responses, simplifying the data collection process. The validation results demonstrate the method's practicality and convenience for real-world engineering applications, showcasing its potential for broad adoption in the field. [ABSTRACT FROM AUTHOR]

During the service life of bridge structures, they will be affected by various physical and environmental changes, which comprehensively manifest on different time scales. In other words, structural effects will have different variation patterns at different time scales, which will be reflected in the multi-scale characteristics of structural mechanical properties over time. However, most of the existing bridge performance indicators are defined on a time scale corresponding to a single factor. Therefore, it is necessary to analyze, process, and apply bridge monitoring signals differently at different time scales and components of different factors. This article proposes a signal decomposition and reconstruction method that extracts signals of different components from strain monitoring signals of bridge structures. On this basis, different feature extraction and indicator recognition are carried out for different extracted components. Among them, for component A related to bridge vibration, this paper proposes a strain mode identification method based on statistical stability. This method is based on the assumption that the strain mode under random noise has approximate time invariant characteristics, and noise is eliminated through statistical averaging. The component B of the vehicle induced effect is obtained through empirical mode decomposition (EMD) and bandpass filtering, and the lateral collaborative performance of the assembled beam bridge is characterized based on the correlation coefficient of component BA bridge hinge joint damage identification method based on temperature strain is proposed for component C of temperature effect. The effectiveness and feasibility of the above workflow were verified through real bridge data. [ABSTRACT FROM AUTHOR]

The intersectional relationship in bridge health monitoring refers to the mapping function that correlates bridge responses across different locations. This relationship is pivotal for estimating structural responses, which are then instrumental in assessing a bridge's service status and identifying potential damage. The current research landscape is heavily focused on high-frequency responses, especially those associated with single-mode vibration. When it comes to low-frequency responses triggered by multi-mode vehicle loading, a prevalent strategy is to regard these low-frequency responses as "quasi-static" and subsequently apply time-series prediction techniques to simulate the intersectional relationship. However, these methods are contingent upon data regarding external loading, such as traffic conditions and air temperatures. This necessitates the collection of long-term monitoring data to account for fluctuations in traffic and temperature, a task that can be quite daunting in real-world engineering contexts. To address this challenge, our study shifts the analytical perspective from a static analysis to a dynamic analysis. By delving into the physical features of bridge responses of the vehicle–bridge interaction (VBI) system, we identify that the intersectional relationship should be inherently time-independent. The perceived time lag in quasi-static responses is, in essence, a result of low-frequency vibrations that are aligned with driving force modes. We specifically derive the intersectional relationship for low-frequency bridge responses within the VBI system and determine it to be a time-invariant transfer matrix associated with multiple mode shapes. Drawing on these physical insights, we adopt a time-independent machine learning method, the backpropagation–artificial neural network (BP-ANN), to simulate the intersectional relationship. To train the network, monitoring data from various cross-sections were input, with the responses at a particular section designated as the output. The trained network is now capable of estimating responses even in scenarios where time-related traffic conditions and temperatures deviate from those present in the training data set. To substantiate the time-independent nature of the derived intersectional relationship, finite element models were developed. The proposed method was further validated through the in-field monitoring of a continuous highway bridge. We anticipate that this method will be highly effective in estimating low-frequency responses under a variety of unknown traffic and air temperature conditions, offering significant convenience for practical engineering applications. [ABSTRACT FROM AUTHOR]

In order to solve the problem of strain modal identification under strain monitoring signals with poor vibration modal information, this paper proposes a strain mode identification method with statistical stability significance. This method removes noise, vehicle-induced effects, and temperature effects from the original dynamic strain signal, retaining only vibration-related components, and obtaining a statistically stable high quality bridge strain power spectrum, thereby identifying high quality strain mode parameters. Furthermore, in order to verify the confidence level of the strain modes obtained by this method, this paper adopts the interval estimation method to estimate the power spectrum, natural frequency, damping ratio, and modal shape after statistical processing. The credibility of strain modes has been estimated by interval estimation. The confidence interval of 95% confidence for each modal parameter is obtained, achieving the confidence-level evaluation of corresponding variable modal parameter identification. In response to practical engineering problems, this paper evaluates the actual bridge data of Tongji Road Bridge in Shanghai, and explains the abnormal phenomena that occurred in the data evaluation based on the measured diseases, verifying the practicality of this method. [ABSTRACT FROM AUTHOR]

The accuracy of bridge performance monitoring and evaluation is easily affected by unfavorable factors such as vehicle coupling and test noise. In order to accurately evaluate the dynamic response and health monitoring threshold of the bridge under different operating conditions, a time-varying dynamic vehicle group model including the main uniform mass and the coupling mass was established, and the influence of road roughness was considered in the coupling equation. A bridge monitoring strategy considering signal noise ratio and vehicle–bridge interaction was proposed, and the effectiveness of the monitoring strategy was verified by taking a simple supported beam as an example. The results showed that the proposed time-varying dynamic vehicle group model could accurately consider the influence of road roughness and estimate the threshold of health monitoring, and the proposed bridge monitoring strategy could filter out a large amount of low signal-to-noise ratio or meaningless data, thus saving computing resources and realizing the lightweight safety monitoring of bridges. [ABSTRACT FROM AUTHOR]

Sliding bearings are a vulnerable component of bridges. Bearing wear will affect the free expansion of the bridge structure and produce greater temperature stress, resulting in damage to the main girder and other components of the bridge. In the process of bridge operation, the timely detection of bearing wear is very important for ensuring structural safety. Therefore, this paper proposes a wear detection method for bridge sliding bearings based on displacement amplitude by eliminating the effects of daily temperature variations. First, the bearing mechanical behavior under temperature effects and the correlation between temperature and bearing friction force are analyzed. The bearing displacement hysteresis model under temperature effects is established, and the variation in thermally induced bearing displacement after wear is obtained. Then, the correlation between temperature and thermally induced bearing displacement is analyzed and, based on the particle swarm optimization (PSO) algorithm and long short-term memory (LSTM) neural network, a multivariate temperature–displacement correlation model is established to achieve the accurate prediction of thermally induced bearing displacement and the elimination of temperature effects. According to the variation in the thermally induced displacement of the bearing after wear, the indicator of thermally induced displacement amplitude errors (TDAE) is proposed, and the cumulative sum (CUSUM) control chart is used to detect the bearing anomaly. Finally, a long-span bridge is analyzed as an example. The results show that the proposed TDAE detection indicator can effectively reflect the sliding friction force of the bridge bearing, and the proposed detection method can accurately detect the sliding bearing wear, which can provide effective information for the bridge caretakers to monitor the occurrence of bearing wear and replace the bearing slide plate in a timely manner. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Southeast University (English Edition) is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While satellite persistent scatterer synthetic aperture radar (SAR) interferometry is an effective technique to monitor the health of structures via selection of long-term coherent pixels, detailed interpretation of displacement measurements requires knowledge of which surfaces, the reflection is coming from. Ray tracing algorithms can be used to simulate SAR backscatter for structures, and link observed PS pixels to specific parts of structures. We investigate the reflectivity of three bridges in London for a high-resolution TerraSAR-X dataset, using a ray tracing technique. Artificial reflectors are mounted on one of the bridges. We compare the simulated backscatter with the location of points selected as PS pixels using a stack of 38 TerraSAR-X images. The results confirm that we can predict overall scattering behavior of a bridge using SAR simulation techniques when we have access to a 3-D model of the structure. However, the results of simulation depend on the level of details in the 3-D model and a high-detailed 3-D model including corner reflectors allows the ray tracing technique to perfectly simulate position of the strong scatterers. This approach can help designers increase the SAR reflectivity of a bridge in the design phase of structural bridge assets, or in a retrofit phase, by installing artificial reflectors. We also link the strong scatterers in the reflectivity map to the corresponding scattering surfaces in the structural model that contributed to the signal. This allows the end-users of the InSAR products to better understand which sections of a bridge are moving when a PS pixel indicates displacement.

Data fusion is an important issue in bridge health monitoring. Through data fusion, specific unknown bridge responses can be estimated with measured responses. However, existing data fusion methods always require a precise finite element model of the bridge or partially measured target responses, which are hard to realize in actual engineering. In this study, we propose a novel data fusion method. Measured inclinations across multiple cross-sections of the target bridge and accelerations at a subset of these sections were used to estimate accelerations at the remaining sections. Theoretical analysis of a typical vehicle-bridge interaction (VBI) system has shown parallels with the blind source separation (BSS) problem. Based on this, Independent Component Analysis (ICA) was applied to derive surrogate inclination mode shapes. This was followed by calculating surrogate displacement mode shapes through numerical integration. Finally, a surrogate inter-section transfer matrix for both measured and unmeasured accelerations was constructed, enabling the estimation of the target accelerations. This paper presents three key principles involving the relationship between the surrogate and actual inter-section transfer matrices, the integration of mode shape functions, and the consistency of transfer matrices for low- and high-frequency responses, which form the basis of the proposed method. A series of numerical simulations and a large-scale laboratory experiment were proposed to validate the proposed method. Compared to existing approaches, our proposed method stands out as a purely data-driven technique, eliminating the need for finite element analysis assessment. By incorporating the ICA algorithm and surrogate mode shapes, this study addresses the challenges associated with obtaining accurate mode shape functions from low-frequency responses. Moreover, our method does not require partial measurements of the target responses, simplifying the data collection process. The validation results demonstrate the method’s practicality and convenience for real-world engineering applications, showcasing its potential for broad adoption in the field.

The intersectional relationship in bridge health monitoring refers to the mapping function that correlates bridge responses across different locations. This relationship is pivotal for estimating structural responses, which are then instrumental in assessing a bridge’s service status and identifying potential damage. The current research landscape is heavily focused on high-frequency responses, especially those associated with single-mode vibration. When it comes to low-frequency responses triggered by multi-mode vehicle loading, a prevalent strategy is to regard these low-frequency responses as “quasi-static” and subsequently apply time-series prediction techniques to simulate the intersectional relationship. However, these methods are contingent upon data regarding external loading, such as traffic conditions and air temperatures. This necessitates the collection of long-term monitoring data to account for fluctuations in traffic and temperature, a task that can be quite daunting in real-world engineering contexts. To address this challenge, our study shifts the analytical perspective from a static analysis to a dynamic analysis. By delving into the physical features of bridge responses of the vehicle–bridge interaction (VBI) system, we identify that the intersectional relationship should be inherently time-independent. The perceived time lag in quasi-static responses is, in essence, a result of low-frequency vibrations that are aligned with driving force modes. We specifically derive the intersectional relationship for low-frequency bridge responses within the VBI system and determine it to be a time-invariant transfer matrix associated with multiple mode shapes. Drawing on these physical insights, we adopt a time-independent machine learning method, the backpropagation–artificial neural network (BP-ANN), to simulate the intersectional relationship. To train the network, monitoring data from various cross-sections were input, with the responses at a particular section designated as the output. The trained network is now capable of estimating responses even in scenarios where time-related traffic conditions and temperatures deviate from those present in the training data set. To substantiate the time-independent nature of the derived intersectional relationship, finite element models were developed. The proposed method was further validated through the in-field monitoring of a continuous highway bridge. We anticipate that this method will be highly effective in estimating low-frequency responses under a variety of unknown traffic and air temperature conditions, offering significant convenience for practical engineering applications.

This paper presents an innovative integrated sensor that combines GNSS and a low-cost accelerometer for bridge health monitoring. GNSS and accelerometers are both significant and effective sensors for structural monitoring, but they each have limitations. The sampling rate of GNSS data is relatively low, making it challenging to capture high-frequency vibrations, while accelerometers struggle with low-frequency signals and are susceptible to environmental changes. Additionally, GNSS receivers and accelerometers are often installed separately, leading to challenges in data fusion processing due to differing temporal and geospatial references. The proposed integrated sensor addresses these issues by synchronizing GNSS and an accelerometer's time and geospatial coordinate reference. This allows for a more accurate and reliable deformation and vibration measurement for bridge monitoring. The performance of the new sensor was assessed using a high-quality/cost Leica GM30 GNSS receiver and a Sherborne A545 accelerometer. Experiments conducted on the Wilford suspension bridge demonstrate the effectiveness of this innovative integrated sensor in measuring deformation and vibration for bridge health monitoring. The limitation of the low-cost MEMS (Micro Electromechanical System) accelerometer for the weak motion frequency detection is also pointed out. [ABSTRACT FROM AUTHOR]

Sliding bearings are a key component of bridges, and their normal performance is an important prerequisite to ensure traffic safety. To solve the problem wherein a single type of sensor has difficulty in effectively predicting the wear life of sliding bearings, this paper proposes a method to predict the sliding bearing wear life using the multitype monitoring data of the bridge. This method uses the vertical acceleration data of the girder with high sampling frequency to calculate the bearing cumulative dynamic displacement under vehicle load and then processes the data collected by the longitudinal displacement gauge with low sampling frequency to extract the bearing cumulative static displacement under the effect of temperature. Next, the daily bearing cumulative displacement calculated by adding them is taken as the evaluation index, and the sliding bearing wear life is predicted based on the reliability analysis. Finally, the proposed method is verified by a numerical example of vehicle–bridge interaction and real bridge monitoring data. The obtained results show that the proposed method can be used to estimate the bearing cumulative displacement with high accuracy and can effectively predict the wear life of sliding bearings. The prediction results can provide an important reference for bridge evaluation. [ABSTRACT FROM AUTHOR]

Small data analytics, at the opposite extreme of big data analytics, represent a critical limitation in structural health monitoring based on spaceborne remote sensing technology. Besides the engineering challenge, small data is typically a demanding issue in machine learning applications related to the prediction of system evolutions. To address this challenge, this article proposes a parsimonious yet robust predictive model obtained as a combination of a regression artificial neural network and of a Bayesian hyperparameter optimization. The final aim of the offered strategy consists of the prediction of structural responses extracted from synthetic aperture radar images in remote sensing. Results regarding a long-span steel arch bridge confirm that, although simple, the proposed method can effectively predict the structural response in terms of displacement data with a noteworthy overall performance. [ABSTRACT FROM AUTHOR]

Monitoring and controlling the vibration of bridge cables can maintain the health of the bridge structure and prolong the service life. This work proposes a novel self‐powered intelligent damper (SID) integrated triboelectric‐electromagnetic hybrid unit for vibration in situ monitoring of stay cables. Among them, the sensing function of the SID is realized by an arched membrane triboelectric nanogenerator (AM‐TENG), and the energy harvesting is realized by a three‐phase electromagnetic generator (TP‐EMG). The working principle and performance are investigated. Rotating and linear experimental platforms are built to verify the SID's low‐frequency (0.25–1.50 Hz) performance, including sensing, power generation, and vibration reduction. The experimental result indicates that the fitting vibration speed coefficient of the bridge cable with the AM‐TENG signal frequency is 0.990. In addition, the TP‐EMG achieves a peak power of 1.21 W. At 0.1 A power supply, the SID can output a damping force of 50 N. Finally, an experimental system is constructed to verify the viability of the SID in bridge cable vibration monitoring and vibration reduction. This work provides a feasible solution for further optimizing the bridge structure health monitoring and maintenance system. [ABSTRACT FROM AUTHOR]

This paper first proposes a novel framework for combining deep learning approach and Synthetic Aperture Radar (SAR) technique to evaluate and predict the condition of bridge displacement. The Long short-term memory (LSTM) neural network and the Small Baseline Subsets InSAR (SBAS-InSAR) are used to predict the longitudinal deformation of the bridge. Firstly, the proposed framework based on LSTM is established to obtain the relationship between the longitudinal deformation of the bridge and the influence parameters (such as temperature, temporal baseline, and others). In particular, the residual connection (RC) module is adapted to form the residual long short-term memory (Res-LSTM) neural network to improve prediction accuracy and robustness. A case study in Nanjing is analyzed to verify the performance of the proposed framework. The extracted datasets obtained from Sentienl-1 are divided into the training, validation, and testing dataset, which can satisfy the requirements in the engineering field. The R 2 and MSE of the testing datasets utilizing the proposed model are 93.17% and 15.62. Furthermore, the results indicated that the proposed framework based on Res-LSTM could predict and warn the abnormal structural deformation, extend the lifespan of structures, and minimize structural damage due to excessive deformation. [ABSTRACT FROM AUTHOR]

Many damage identification methods are based on a comparison of structure response between healthy and damaged states. However, the healthy state response of especially old bridges is rarely available. In this paper, a new output-only damage identification approach is proposed to locate damage in multi-span bridges. In this method, only the damaged state strain and displacement influence lines are required. This approach is theoretically explained for a three-span girder. The middle span, the health condition of which is in quest, is modeled as a single-span beam, supported by rotational springs with unknown stiffness. A new algorithm is proposed to estimate the healthy state displacement influence line of the middle point of the span. In this algorithm, the ratio of the flexural stiffness of the girder to the spring's rotational stiffness is determined in such a way that the estimated displacement, is as close to the measured response, as possible. Then, the estimated displacement is scaled based on two constraints. First, it is expected to be lower than the pseudo-static component of measured displacement because damage increases the response globally. Second, the difference between the scaled and pseudo-static component of the measured response is minimized. Eventually, damage is located using a novel damage index based on the local deviation of pseudo-static component of measured displacement from the estimated scaled response. The proposed method is numerically evaluated in a three-span girder. The damage is successfully identified in all different scenarios. Also, its application to Powder Mill Pond Bridge is numerically investigated which yielded promising results. [ABSTRACT FROM AUTHOR]

The accuracy of bridge performance monitoring and evaluation is easily affected by unfavorable factors such as vehicle coupling and test noise. In order to accurately evaluate the dynamic response and health monitoring threshold of the bridge under different operating conditions, a time-varying dynamic vehicle group model including the main uniform mass and the coupling mass was established, and the influence of road roughness was considered in the coupling equation. A bridge monitoring strategy considering signal noise ratio and vehicle–bridge interaction was proposed, and the effectiveness of the monitoring strategy was verified by taking a simple supported beam as an example. The results showed that the proposed time-varying dynamic vehicle group model could accurately consider the influence of road roughness and estimate the threshold of health monitoring, and the proposed bridge monitoring strategy could filter out a large amount of low signal-to-noise ratio or meaningless data, thus saving computing resources and realizing the lightweight safety monitoring of bridges.

Bridge health monitoring (BHM) has grown as an essential component of the maintenance paradigm in any transportation network around the world. The mostly used visual inspection method has limitations such as inspector bias, inaccessibility in remote areas, need for human physical presence at the bridge location, etc. On the other hand, vibration-based BHM methods require artificial excitation to the bridge. This motivates researchers to explore potential alternatives for BHM. The bridge weigh-in-motion (B-WIM) system has emerged as a promising alternative with the potential to augment traditional BHM techniques. The advantages of B-WIM systems are durability, portability, easy installation, etc. In addition to weight estimation of passing vehicles, it provides other structural informations that are helpful for bridge assessment. It also overcomes the limitations of pavement-based WIM systems. Most importantly, a B-WIM system uses the same components as a standard BHM system, making BHM a by-product and economical compared to a standalone BHM system. With the development of B-WIM theory, recent researches have revealed the feasibility of B-WIM system for BHM. However, the applicability of the B-WIM system for BHM in any type of bridge and condition is limited. Through the fundamentals of B-WIM theory, this paper reviews the existing bridge response-based BHM methods that involved B-WIM algorithm. The findings are discussed in order to identify the remaining challenges and provide insight to the future aspects. [ABSTRACT FROM AUTHOR]

Digital twin aims to create a virtual model for a physical structure by combining measurement data in structural health monitoring. The most important feature is to achieve the physical structure-monitoring data synchronization. For this purpose, a physics-data hybrid framework to develop the bridge digital twin model in structural health monitoring is proposed in the paper. The physical base is firstly formed by the finite element model of the digital representation for the physical bridge that can fully incorporate both structural geometry and structural state. The data base is then built by all measurement data of the monitored bridge. By defining the context that is common to both physical base and data base, the mirror relationship between physical base and data base for the specified context is formulated. To achieve the best matching of the mirror relationship by minimizing process, the digital twin model in terms of the specified context can be developed. In such a way, the proposed framework integrates physical knowledge and data intelligence into one model. A demonstration of a simulated simply supported beam is provided to show how the digital twin model is developed by using proposed physics-data hybrid framework. It is found that the generated digital twin model is consistent with the current structural state of the beam. The presented physics-data hybrid framework helps in clearer understanding of the realization of digital twin model in structural health monitoring, providing a new perspective for smart bridge solutions. [ABSTRACT FROM AUTHOR]

Video magnification has received increased attention in structural deformation measurement. Current video magnification methods typically chose the Eulerian perspective rather than the Lagrangian, which requires tedious work for tedious parameter adjustment. However, the low computational efficiency is a prominent problem of the Euler-based method due to the complex time domain filtering processing. To address this issue, a novelty motion magnification algorithm, called Euler fast motion detection (EFMD), is proposed in this paper, which can realize real-time measurement with low computation cost. Laboratory verification and field experiments were carried out to verify the accuracy of the algorithm in deck and cable dynamic response measurement. In laboratory experiments, the error of natural frequency measured by the proposed method is within 2%. In the field experiments, the small displacements (about 1.5 mm) can be achieved by the proposed method at a distance greater than 20 m, and the error of the cable vibration is less than 4%. [ABSTRACT FROM AUTHOR]

Abstract This article presents a new data-driven reliability analysis framework through combining the sample convolution and interaction network (SCINet) with Bayesian probability recursion to predict the time-variant reliability of bridge girders. The structural response predicted by SCINet and the state parameter (i.e., the variance of normal distribution) estimated by Bayesian dynamic linear model (BDLM) are used to form the limit state function to predict time-variant reliability. The results with a practical bridge show that the proposed method can predict future structural responses and time-variant reliability more accurately than BDLM, long short-term memory (LSTM), and long- and short-term time-series network (LSTNet) methods. [ABSTRACT FROM AUTHOR]

With the development of optical frequency-domain reflectometry (OFDR)-based distributed strain sensing technology and static influence line (IL) measurement concept, the special influence surface (IS) measurement, a function of sensing and force locations, has become technically feasible. This study investigated, for the first time, damage detection in long-span bridges using the special IS constructed on the basis of the OFDR-based distributed sensing. The damage detection performance of IS was validated through numerical and experimental case studies on a scaled physical Tsing Ma bridge (TMB) model. First, the special IS concept was elaborated. The scaled TMB model was then introduced as a representative long-span bridge. Subsequently, the IS characteristics of different bridge components of the scaled TMB were analyzed using the finite element model (FEM). Various IS-based indices, including strain IS (SIS) and displacement IS (DIS), were applied to hypothetical damage scenarios where the bottom and top chords were subjected to severe damage in the FEM. The extracted ISs successfully realized damage localization with relatively great sensitivities. For comparison, the modal parameters were also output for damage detection. In the experimental section, an OFDR-based distributed sensor was placed along the top chord of one middle grid in the scaled TMB model to construct the SIS. The SIS changes could accurately localize the single, double, and triple bottom-chord damages. The numerical and experimental results demonstrated the feasibility and effectiveness of the OFDR-constructed special SIS for damage detection, indicating that IS is a promising damage indicator for long-span bridges, promoting the development of distributed sensing techniques in structural health monitoring for real-bridge applications. [ABSTRACT FROM AUTHOR]

In this paper, an extended influence line parameter under the action of statistical steady-state traffic flow loads is defined, which is called the vehicle-induced effect influence line characteristic function (ILF). It is theoretically proved that this function can characterize the static characteristics of bridge structures, and can also be used to track and monitor the evolution of static characteristics of bridge structures throughout their service periods. On this basis, this paper presents a set of methods to identify this function from monitoring big data, mainly based on extracting the static effect of a single vehicle on the structure, including methods such as empirical mode decomposition (EMD), spectrum analysis, and mode reorganization. The set of methods only requires monitoring structural effects, without the need for traffic load information, which greatly reduces the requirements for the monitoring system, with simple implementation and high operability. Through the application of the monitoring data of the actual bridge, the results show that the recognition effect of the vehicle-induced strain influence line characteristic function (IL F ε ) by the proposed method is in line with the theoretical expectation, and it is suitable for the actual complex and changeable monitoring environment with good robustness. The identification technology of ILF proposed in this paper can be further used to study a new index system to characterize the static characteristics of the structure, thus opening up a new methodological field for the intelligent perception and diagnosis of the structure under the condition of monitoring big data. [ABSTRACT FROM AUTHOR]

A visual inspection, which entails field surveying, such as photodocumentation and footage, is the first step of a multi-level approach to bridge health assessment. Furthermore, the use of surface models, CAD drawings, and orthophotos, guarantees complete and accurate documentation, thus allowing for a better understanding of the environment, the anthropic structures, and their relationships. All the georeferenced surveys' outputs are especially advisable within a prespective of periodical monitoring, as forseen by national legislation. The work is aimed to study two adjacent bridges over the Arno River in Lastra a Signa, Italy. One of the bridges, Ponte nuovo sull'Arno, is an overpass for motor vehicles. The other one, which is called Passarella sull'Arno, is a pedestrian viaduct. A topographic reference network has been settled using the GNSS survey technique. 3D point clouds of the bridges have been acquired by performing a Laser Scanning survey. A bathymetric survey has been carried out to acquire a 3D point cloud of submerged bridges' parts and the riverbed. Through a Photogrammetric survey from RPAS, an orthophoto of the area has been built. Finally, evidence of historical submerged bridge structures has been identified thanks to the multi beam survey. The work's objective is to integrate surveying geomatics techniques to create a reliable survey of the bridges, the surrounding area, and the riverbed, as support to the most common structural health assessment methods. [ABSTRACT FROM AUTHOR]

Aiming at comprehensively evaluating the status of a bridge monitoring system, an evaluation framework based on the improved Delphi, analytic Hierarchy process, Grey relations analysis and Fuzzy integrated evaluation (DHGF) is selected. Firstly, the evaluation indexes for the bridge monitoring system are determined by an anonymous group discussion and expert questionnaire using the improved Delphi method. Secondly, a comparison matrix of the evaluation indexes is constructed to determine the comprehensive weight via the analytic hierarchy process. Then, based on the gray relations analysis, the albino weight function is constructed, the evaluation gray class is determined, and the single-factor fuzzy evaluation matrix is obtained. Finally, the final evaluation result was obtained by the fuzzy comprehensive evaluation. The evaluation results of a real bridge monitoring system show that the evaluation level of the monitoring system was level II, and the proposed framework could better reflect the construction and operation status of the monitoring system. [ABSTRACT FROM AUTHOR]

Structural damage is inherent in civil engineering structures, and bridges are no exception. It is vital to monitor and keep track of damage in bridge structures as a result of multiple mechanical, environmental and traffic-induced factors. Monitoring the formation and propagation of structural damage is also pertinent for enhancing the service life of bridges. Bridge health monitoring (BHM) has always been an active research area for engineers and stakeholders. While all monitoring techniques intend to provide accurate and decisive information on the remaining useful life, safety, integrity and serviceability of bridges, maintaining uninterrupted operation of a bridge relies strongly on understanding the development and propagation of damage. BHM methods have been extensively researched on bridges over the decades, and new methodologies have started to be used by domain experts, especially within the last decade. Emerging methods, as the products of technology advancements, have resulted in handy tools that have been quickly adopted by bridge engineers. State-of-the-art techniques such as lidar, photogrammetry, virtual reality, augmented reality, digital twins, computer vision, machine learning and deep learning are now integrated parts of the new-generation of BHM operations. This paper presents a brief overview of these latest BHM technologies. [ABSTRACT FROM AUTHOR]

In bridge health monitoring, in order to closely monitor the structural state changes of the bridge under heavy traffic load and other harsh environments, the monitoring system is required to give the change process of structural modal parameters. Due to the symmetric variables of bridge monitoring during operation, the evaluation needs to be completed by the recursive identification of modal parameters based on environmental excitation, especially the recursive recognition of the random subspace method with high recognition accuracy. We have studied the recursive identification methods of covariance-driven and data-driven random subspace categories respectively, established the corresponding recursive format, and used the model structure of the ASCE structural health monitoring benchmark problem as a numerical example to verify the reliability of the proposed method. First, based on the similar interference environment of the observation data at the same time, a reference point covariance-driven random subspace recursive algorithm (IV-RSSI/Cov) based on the auxiliary variable projection approximation tracking (IV-PAST) algorithm is established. The recursive format of the system matrix and modal parameters is obtained. Based on Givens rotation, the rank-2 update form of the row space projection matrix is established, and the recursive format of the data-driven recursive random subspace method (RSSI/Data) under the PAST algorithm is obtained. Then, based on the benchmark problem of ASCE-SHM, the response of the model structure under environmental excitation is numerically simulated, the frequency, damping ratio and vibration mode of the structure are recursively tracked, and their reliability and shortcomings are studied. After improving the recursive method, the frequency tracking accuracy has been improved, with a maximum accuracy of 99.8%. [ABSTRACT FROM AUTHOR]

The multifractal theory is applied in an analysis of bridge disturbance signals with the aim of investigating their nonlinear characteristics, and then the recognisable fault features are extracted from them. By calculating the box dimension and correlation dimension of the bridge disturbance signal, the dimensional characteristics of the disturbance data are analysed to distinguish the health-state of the bridge. Finally, taking the bridge disturbance data as an example, and by using the multifractal spectrum analysis of the disturbance data, it is concluded that the multifractal method can accurately identify the fault state and realise the bridge health monitoring.

Abstract Along with the advancement in sensing and communication technologies, the explosion in the measurement data collected by structural health monitoring (SHM) systems installed in bridges brings both opportunities and challenges to the engineering community for the SHM of bridges. Deep learning (DL), based on deep neural networks and equipped with high-end computer resources, provides a promising way of using big measurement data to address the problem and has made remarkable successes in recent years. This paper focuses on the review of the recent application of DL in SHM, particularly damage detection, and provides readers with an overall understanding of the missions faced by the SHM of the bridges. The general studies of DL in vibration-based SHM and vision-based SHM are respectively reviewed first. The applications of DL to some real bridges are then commented. A summary of limitations and prospects in the DL application for bridge health monitoring is finally given.

Abstract In bridge structural health monitoring, typically the dynamic response of the system is used to assess the health condition of the bridge. However, the dynamic interaction between a bridge and a passing vehicle imposes non-stationarity on the system response, whereby the bridge modal parameters become time-dependent and detecting damage, for example, based on the bridge modal parameters, becomes challenging. Dynamic vehicle-bridge interaction (VBI) responses have mainly been investigated for damage detection through identifying signal singularities and abrupt changes. The singularities are usually associated with high-frequency components (relative to the bridge natural frequencies), and it is demanding to isolate the damage-induced singularities from those caused by either an operational condition, i.e., track irregularities, or noise. Unlike the high-frequency range, the influence of damage on the resonance frequency of the coupled system has not been fully explored. The present study proposes the shape of the bridge instantaneous frequency as a damage sensitive feature in which the influence of the vehicle dynamics can be excluded. This study demonstrates the feasibility of a damage detection approach based on the bridge instantaneous frequency by applying Wavelet Synchrosqueezed Transform (WSST). In this approach the bridge instantaneous frequency variation induced by damage is distinguished from the bridge instantaneous frequency variation induced by the vehicle. Several damage scenarios that are implemented numerically are analyzed to verify the method’s performance. The results demonstrate that a high resolution instantaneous frequency extracted from the VBI dynamic response outperforms the resonance frequency in determining the local disruption, leading to detecting the damage. A Damage Index (DI) is also proposed as an attempt to quantify the damage severity.

Monitoring bridges through vibration responses of drive-by vehicles enables efficient and low-cost bridge maintenance by allowing each vehicle to inspect multiple bridges and eliminating the needs for installing and maintaining sensors on every bridge. However, many existing drive-by monitoring approaches are based on supervised learning models that require massive labeled data from every bridge. It is expensive and time-consuming, if not impossible, to obtain these labeled data. Furthermore, directly applying a supervised learning model trained on one bridge to new bridges would result in low accuracy due to the shift between different bridges' data distributions. Moreover, when we have multiple tasks (e.g., damage detection, localization, and quantification), the distribution shifts become more challenging than having only one task because different tasks have distinct distribution shifts and varying task difficulties. To this end, we introduce HierMUD, the first Hier archical M ulti-task U nsupervised D omain adaptation framework that transfers the damage diagnosis model learned from one bridge to a new bridge without requiring any labels from the new bridge. Specifically, our framework learns a hierarchical neural network model in an adversarial way to extract features that are informative to multiple tasks and invariant across multiple bridges. To match distributions over multiple tasks, we design a new loss function based on a newly derived generalization risk bound to adaptively assign higher weights to tasks with more shifted distributions. To learn multiple tasks with varying task difficulties, we split them into easy-to-learn and hard-to-learn tasks based on their distributions. Then, we formulate a feature hierarchy to utilize more learning resources to improve the hard-to-learn tasks' performance. We evaluate our framework with experimental data from 2 bridges and 3 vehicles. We achieve up to 2X better performance than baseline methods, including average accuracy of 95% for damage detection, 93% for localization, and 0.38 lbs mean absolute error for quantification. [ABSTRACT FROM AUTHOR]

Bridges are integral parts of the infrastructure and play a major role in civil engineering. Bridge health monitoring is necessary to extend the life of a bridge and retain safety. Periodic monitoring contributes significantly in keeping these structures operational and extends structural integrity. Different researchers have proposed different methods for identifying bridge damages based on different theories and laboratory tests. Several review papers have been published in the literature on the identification of damage and crack in bridge structures in the last few decades. In this paper, a review of literature on damage identification in bridge structures based on different methods and theories is carried out. The aim of this paper is to critically evaluate different methods that have been proposed to detect damages in different bridges. Different papers have been carefully reviewed, and the gaps, limitations, and superiority of the methods used are identified. Furthermore, in most of the reviews, future applications and several sustainable methods which are necessary for bridge monitoring are covered. This study significantly contributes to the literature by critically examining different methods, giving guidelines on the methods that identify the damages in bridge structures more accurately, and serving as a good reference for other researchers and future works. [ABSTRACT FROM AUTHOR]

Recent decades have witnessed a rise in interest in bridge health monitoring utilizing the vibrations of passing vehicles. However, existing studies commonly rely on constant speeds or tuning vehicular parameters, making their methods challenging to be used in practical engineering applications. Additionally, recent studies on the data-driven approach usually need labeled data for damage scenarios. Still, getting these labels in engineering is difficult or even impractical because the bridge is typically in a healthy state. This paper proposes a novel, damaged-label-free, machine-learning-based, indirect bridge-health monitoring method named the assumption accuracy method (A 2 M). Initially, the raw frequency responses of the vehicle are employed to train a classifier, and K-folder cross-validation accuracy scores are then used to calculate a threshold to specify the bridge's health state. Compared to merely focusing on low-band frequency responses (0–50 Hz), utilizing full-band vehicle responses can significantly improve the accuracy, meaning that the bridge's dynamic information exists in the higher frequency ranges and can contribute to detecting bridge damage. However, raw frequency responses are generally in a high-dimensional space, and the number of features is much greater than that of samples. To represent the frequency responses via latent representations in a low-dimension space, appropriate dimension-reduction techniques are therefore, needed. It was found that principal component analysis (PCA) and Mel-frequency cepstral coefficients (MFCCs) are suitable for the aforementioned issue, and MFCCs are more damage-sensitive. When the bridge is in a healthy condition, the accuracy values obtained using MFCCs are primarily dispersed around 0.5, but following the occurrence of damage, they increased significantly to 0.89–1.0 in this study. [ABSTRACT FROM AUTHOR]

The bridge deflection data measured in field are greatly affected by temperature. In some situations, temperature can be the dominant factor comparing with loads and other factors. Generally, dynamic deflection contains information related to the health condition of bridge. It is wildly used in dynamic deflection warning system. The temperature-induced deflection might lead to the inaccuracy of such system. Therefore, it is important to separate the temperature-induced deflection when implement bridge health condition assessment using dynamic deflection data. However, the accuracy of existing methods of temperature-induced deflection separation is low. This low accuracy is normally caused by mode aliasing and excessive elimination of feature components, etc. To this end, this study proposed an innovative method of temperature-induced deflection separation based on the time-varying filtered empirical mode decomposition (TVFEMD), permutation entropy (PE) and Kullback–Leibler divergence (KLD). The proposed method is used to extract temperature-induced deflection from bridge deflection data. It overcomes the problems of mode aliasing and end-point effects by reconstructing the signals with the same composition. The separating accuracy is also improved by the non-linear feature extracting ability of the proposed method. First, TVFEMD was introduced to decompose the original bridge deflection data into a number of intrinsic mode functions (IMFs). Then, PE was adopted to measure the complexity of each IMF, by which all IMFs can be reconstructed into several new subseries. Furthermore, KLD was employed to calculate the divergence values between these reconstructed subseries and the measured ambient temperatures. The subseries with the smallest divergence value can be considered to be the temperature-induced deflection. Finally, numerical study and locale measurement were utilized to comprehensively validate the effectiveness of the proposed method. The results show that the identification accuracy of the proposed method exceeds 90%. It outperforms the other involved models in terms of separating daily and annual temperature-induced deflection. [ABSTRACT FROM AUTHOR]

This paper presents an innovative integrated sensor that combines GNSS and a low-cost accelerometer for bridge health monitoring. GNSS and accelerometers are both significant and effective sensors for structural monitoring, but they each have limitations. The sampling rate of GNSS data is relatively low, making it challenging to capture high-frequency vibrations, while accelerometers struggle with low-frequency signals and are susceptible to environmental changes. Additionally, GNSS receivers and accelerometers are often installed separately, leading to challenges in data fusion processing due to differing temporal and geospatial references. The proposed integrated sensor addresses these issues by synchronizing GNSS and an accelerometer’s time and geospatial coordinate reference. This allows for a more accurate and reliable deformation and vibration measurement for bridge monitoring. The performance of the new sensor was assessed using a high-quality/cost Leica GM30 GNSS receiver and a Sherborne A545 accelerometer. Experiments conducted on the Wilford suspension bridge demonstrate the effectiveness of this innovative integrated sensor in measuring deformation and vibration for bridge health monitoring. The limitation of the low-cost MEMS (Micro Electromechanical System) accelerometer for the weak motion frequency detection is also pointed out.

Along with the advancement in sensing and communication technologies, the explosion in the measurement data collected by structural health monitoring (SHM) systems installed in bridges brings both opportunities and challenges to the engineering community for the SHM of bridges. Deep learning (DL), based on deep neural networks and equipped with high-end computer resources, provides a promising way of using big measurement data to address the problem and has made remarkable successes in recent years. This paper focuses on the review of the recent application of DL in SHM, particularly damage detection, and provides readers with an overall understanding of the missions faced by the SHM of the bridges. The general studies of DL in vibration-based SHM and vision-based SHM are respectively reviewed first. The applications of DL to some real bridges are then commented. A summary of limitations and prospects in the DL application for bridge health monitoring is finally given. [ABSTRACT FROM AUTHOR]

• A hybrid modal identification is proposed using NExT and EFD. • It can tackle closely spaced and low energy frequencies. • This study is validated with numerical, experimental, and full-scale studies. • The results are compared with the traditional blind source separation method. Bridge health monitoring has been a prominent focus within the global engineering community. Bridge owners, stakeholders, and engineers face the formidable tasks of ensuring efficient monitoring, conducting reliable data analysis, interpreting data logically, and making timely decisions. With the increasing global infrastructure deficit, there is an ever-increasing need to develop reliable and economical bridge monitoring solutions. In this paper, a bridge condition assessment technique is proposed that can utilize the vibration data collected from the instrumented sensors and provide reliable system identification results. The proposed method develops a hybrid approach by integrating the Natural Excitation Technique (NExT) and Empirical Fourier Decomposition (EFD) to analyze ambient bridge vibration data and determine the modal parameters of the bridge. First, NExT is formulated to determine the cross-correlation functions of the bridge measurements, and then EFD is explored to decompose the signals into their monocomponents to identify the bridge modal parameters. The proposed methodology can overcome mode mixing and perform modal identification of a system with closely spaced frequencies and low energy modes. The estimated modal parameters such as bridge frequencies, mode shapes, and damping ratio are used for condition assessment of numerical, experimental and full-scale structures, including a short-span steel bridge located in Ontario, Canada. The results demonstrate that the proposed methodology can provide accurate and robust estimates of bridge modal parameters. Future research is reserved for real-time implementation of the proposed methodology for a wide range of civil structures. [ABSTRACT FROM AUTHOR]

Deep Learning has been increasingly utilized in structural damage detection. Existing relevant studies often highlight the benefits of supervised deep learning in the intelligent identification of bridge damage. Notably, however, supervised deep learning methods encounter specific challenges in processing real-world monitoring data to reflect damage. Typical challenges include: (i) the need for a large dataset derived from a vast number of labeled damaged cases, which are extremely difficult to obtain from real-world monitoring data, experiments, and numerical simulations; (ii) the inability of available damage sample spectra to fully capture the damage information underlying the dynamic responses of bridges; (iii) the likelihood of requiring manual intervention to discriminate damage from the outputs of deep learning models, which is inefficient when dealing with massive amounts of monitoring data. To address these challenges, this study proposes an intelligent unsupervised deep learning framework for damage identification in bridges. The framework is characterized by three innovative technical elements: (1) a Deep Convolutional Autoencoder (CAE) model with a hybrid loss function is developed to provide an intelligent system that can identify bridge damage without requiring a large dataset of damaged cases; (2) Wavelet transmissibility pattern spectra are established to characterize damage information embedded in dynamic responses in a more efficient manner; and (3) an Ordering Points To Identify the Clustering Structure (OPTICS)-based damage picker is proposed to achieve automatic discrimination of damage cases. The feasibility of this framework is numerically demonstrated through the detection of damage in a curved bridge, and its effectiveness is experimentally validated on a laboratory-scale suspension bridge with induced damage. The results indicate that the proposed framework can automatically and accurately identify damage in bridges. This framework provides a solution for intelligent and data-driven bridge damage detection. [ABSTRACT FROM AUTHOR]

This study looks at the load distribution among beams in a 3-dimensional bridge Finite Element Model under normal traffic loading and the resulting effect of damage, modelled as a localised loss of elements in a beam. The load distribution is captured, both transversely across the girders and also longitudinally along the girders, and used to identify damage that is remote from the strain sensor location. When there is a loss of elements in a section of the bridge (e.g. due to a strike from a vehicle passing underneath), a portion of the load will redistribute along alternative load paths in the structure. This is most pronounced when the position of the loading and the location of the damage align but also happens when they do not. The study examines the effect of vehicles of different weight and axle configuration on the load distribution, and how grouping results by axle configuration can overcome this. A further novelty of this approach is that the strain response can be transformed into an influence line, which then allows data from large populations of general traffic to be used irrespective of axle configuration. The effect of various transverse positions of vehicles within the driving lane is overcome by combining data from large populations of vehicles. An advantage of this method is that the load distribution from a vehicle crossing is calculated as a ratio i.e. when the bridge is experiencing the same general environmental conditions, and thus reducing the difficulty in controlling the effects of varying environmental conditions when comparing results to a healthy baseline. • Monitoring of load redistribution both transversely and longitudinally along girders. • Use of the whole strain signal from a vehicle crossing (i.e. not just maximum strain). • The bridge response to vehicles can be transformed into a strain axle influence line. • Large populations of general traffic can be used. • Small deviations in transverse position of vehicles overcome. [ABSTRACT FROM AUTHOR]

Due to variations in wind speed profiles along the length of bridge stay cables, vortex-induced vibrations (VIV) exhibit multimodal characteristics, presenting challenges for VIV identification. Currently, the VIV identification is concentrated on the stable stage of VIV, lacking an available early warning system for detecting the initial developing stage of VIV. In this study, a deep learning-based approach that integrates energy distribution ratio features derived from frequency band wavelet packet decomposition to recognize VIV of stay cable was proposed. Firstly, vibration characteristics induced by vortices in cable-stayed bridges were analyzed based on field monitoring data from the bridge health monitoring system, aiming to propose suitable feature indicators for VIV identification. Secondly, using root mean square as label classification, a deep learning model was constructed, incorporating convolutional neural networks, long short-term memory networks, and attention mechanisms. Finally, four different stages in the evolution of stay cables VIV were identified utilizing field monitoring datasets to analyze the optimal parameter. Meanwhile, effective early warning recognition was achieved through the classification and recognition of confusion matrix. This study provides technical support for early warning systems and structural condition assessment concerning bridge stay cable VIV. [ABSTRACT FROM AUTHOR]

This paper is dedicated to address the limitations of vision-based displacement monitoring (VDM) methods when monitoring multiple targets. It first introduces a two-dimensional (2D) VDM method for automated monitoring of multiple target displacements, eliminating the need for manual template selection and repeated camera calibration. The method uses coded artificial targets supplemented by intelligent algorithms and self-calibration techniques. A three-dimensional (3D) VDM method is then developed for displacement monitoring using two independent cameras. The efficacy and precision of the proposed methods were extensively validated through numerical simulations and experimental studies. The numerical simulation results show that coded targets can be accurately detected and identified. Shaker test results show that the proposed 2D VDM method effectively monitors multiple encoded targets in different planes and autonomously complete the whole process. The proposed 3D VDM method accurately monitors the 3D displacements of multiple targets on a bridge using only two independent cameras. • This paper presents a vision-based displacement monitoring method using coded artificial targets. • 2D and 3D displacements can be measured automatically using computer vision techniques. • Algorithms for automatic detection, identification and tracking of coded targets are developed. • Numerical and experimental verifications are presented. [ABSTRACT FROM AUTHOR]

Investigating the bridge dynamic characteristics under operational traffic load is critical for bridge health monitoring. As vehicles traverse the bridge, the bridge frequencies present time-varying characteristics owing to the effect of vehicle-bridge dynamic interaction. The recently introduced variational nonlinear chirp mode decomposition (VNCMD) demonstrates significant benefits in identifying structural instantaneous frequencies (IFs) from the measured responses. However, its practical application is limited by the requirement for artificially setting priori parameters (i.e., upper bound of noise level and number of modal components). In this study, a modified VNCMD (MVNCMD) is proposed to overcome this limitation and is employed to identify the IFs of the bridge under moving vehicle using single sensor data. First, by modifying the optimization function, the proposed MVNCMD adopts a novel algorithm framework that eliminates the necessity for presetting the upper bound of noise level. An autoregressive spectrum-based method is then introduced to preset the number of modal components. The proposed MVNCMD is evaluated using a synthetic non-stationary signal, demonstrating its effectiveness in identifying IFs and overcoming the limitations of the original VNCMD. Furthermore, the numerical simulation involving different parameter analysis and laboratory experiments of the coupled vehicle-bridge system are conducted to validate the effectiveness of the proposed method. It is to be shown that the proposed MVNCMD method is capable of identifying the IFs of the bridge under moving vehicle using single sensor data and outperforms other time-frequency methods in terms of identification accuracy, which provides a robust tool for analyzing the time-varying characteristics of the vehicle-bridge interaction system. • Novel time-frequency method for time-varying characteristics analysis of bridge under moving vehicle with limited sensors. • Modified VNCMD for overcoming the difficulty of presetting priori parameters of original VNCMD. • Numerical simulation and laboratory experiments for demonstrating the effectiveness of the proposed method. • Discussing the effects of vehicle mass, vehicle speed, and road surface roughness on bridge frequencies. [ABSTRACT FROM AUTHOR]