Bridge Health Monitoring Research Articles

Bayesian-guided operational modal identification of a highway bridge considering non-uniform sampling

During the structural health monitoring of bridges, it has been observed that the vibration data collected can sometimes be randomly lost or sampled non-uniformly. This leads to a low signal-to-noise ratio in the spectral functions of the measured data, making it difficult to identify weak modes. To address this issue, a framework for operational modal identification is proposed in this study. It utilizes the fast Bayesian fast Fourier transform (FFT) method to estimate the modal parameters of highway bridges considering the non-uniform monitoring data. The initial frequency parameters for the fast Bayesian FFT approach are automatically determined using the proposed autoregressive (AR) power spectral density (PSD)-guided peak picking method. This overcomes the challenge of capturing initial frequencies related to weakly contributed modes. Additionally, the bandwidth parameter for each mode is determined using the modal assurance criterion (MAC) of the first left singular vectors of PSD matrices. Furthermore, when analyzing non-uniform vibration data, it is recommended to use the non-uniform FFT (NUFFT) for calculating PSD functions in order to improve identification accuracy. The proposed method is validated using acceleration data from both a numerical model and a real-world bridge. The results demonstrate that the identification uncertainty of modal parameters increases with higher non-uniform levels.

Health monitoring and maintenance of highway and bridge

In recent years, due to the rapid growth of traffic volume, the use frequency and load of Bridges have been significantly increased, and the service state of the bridge structure has deteriorated sharply. At present, there is no corresponding standard specification for the health monitoring and maintenance of Bridges in China, so the safety, durability and normal performance of Bridges are still the focus of people's attention. This paper summarizes the research results of health monitoring and maintenance at home and abroad, summarizes and analyzes the highway bridge health monitoring and maintenance, and introduces the bridge health monitoring system in China.

Structural health monitoring of a swivel bridge for evaluating builder comfort under train-induced vibration

The swivel bridge being built overcrossing the railway service line, the builder’s comfort level under the train-induced vibration is one of the critical issues in the bridge construction process. This study compares the applicability and usage limits of existing vibration comfort evaluation indicators for reasonably selected vibration comfort evaluation criteria. A framework is therefore proposed to evaluate builder comfort under train-induced vibrations during the construction process. Taking a swivel bridge overcrossing the Shanghai-Kunming railway line as an example, the structural health monitoring system is performed in three construction stages of the swivel bridge. Then, the acceleration response is measured, the acceleration-based evaluation indicators are compared, and the builder comfort level is evaluated in the three construction stages. The results demonstrate that the on-site measured indicators are close to the standard limits, and the builder comfort on the swivel bridge under the excitation of passing trains should be given attention.

Whole-process analysis and implementation of a self-powered wireless health monitoring system for railway bridges: Theory, simulation and experiment

In this work, a self-powered wireless health monitoring system (SP-WHMS) combined with the Internet-of-Things (IoT) is proposed and implemented for a long-term and long-distance health monitoring of railway bridges. This system frees the wireless sensor nodes (WSNs) from their dependence on chemical batteries by using the piezoelectric energy harvester (PEH) to harvest the vibration energy from bridges to power the WSNs. The whole-process of the SP-WHMS including energy conversion, energy storage, energy management and application is analyzed at the first time through theory, simulation and experiment. For the energy conversion, a reported PEH model with high energy conversion efficiency (called D-M PEH) is refabricated, which can achieve a maximum average output power of 0.9 W under unit harmonic acceleration excitation. For the energy storage, a high-precision iteration analysis procedure (IAP) is proposed to predict the charging process of the strong-coupled PEHs and the charging process under non-harmonic excitations. The comparison results show that the IAP’s prediction deviates less than 3% from the experiment result and even less than 1% from the simulation result. For the energy management, an AP64500 chip with two adjustable input voltage thresholds and a stable output voltage ensures the normal operation of the SP-WHMS and makes the SP-WHMS suitable for different types of WSNs. For the application, the synergies between energy conversion, storage and management are considered, and the effectiveness of the SP-WHMS used on railway bridges is tested through an activation experiment. This work provides a technical guidance and framework for the implementation of the self-powered wireless monitoring on railway bridges.

Research Progress and Future Prospects of Bridge Health Monitoring

Research Progress and Future Prospects of Bridge Health Monitoring

Time-varying characteristics analysis of bridge under moving vehicle using a modified time-frequency method with limited sensors

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.

Introduction and application of a drive-by damage detection methodology for bridges using variational mode decomposition

In this research, the variational mode decomposition (VMD) method is used for the drive-by health monitoring of bridges. Firstly, the problem of a half-trailer tractor moving over a bridge is formulated. Next, a Finite Element (FE) code is developed and verified against modal analysis results where complete agreement is found. The vehicle's output signals are decomposed through VMD and then analyzed to identify and precisely locate damage in the bridge structure. The range of applicability of this technique is examined from different perspectives by including various road classes, damage severity and location, and noise. The results prove the robustness and reliability of using VMD for drive-by damage detection. The method outcomes indicate that through the VMD method, cracks with a depth of 10% to 20% of the beam height can be detected even in the case of a rough road profile. A comparison of the results of the VMD and the well-known empirical mode decomposition (EMD) method has also been conducted. This comparison reveals that by implementing the VMD, precise damage locations can be determined, whereas the EMD fails to detect any damage under the conditions considered in this study. The effects of noise and moving vehicle speed are also investigated in the research, and it is found that processing the output signals using VMD can yield reliable estimates of the damage location(s).

Complete Ensemble Empirical Mode Decomposition and Wavelet Algorithm Denoising Method for Bridge Monitoring Signals

In order to investigate the analysis and processing methods for nonstationary signals generated in bridge health monitoring systems, this study combines the advantages of complete ensemble empirical mode decomposition (CEEMD) and wavelet threshold denoising algorithms to construct the CEEMD–wavelet threshold denoising algorithm. The algorithm follows the following steps: first, add noise to the monitoring data and obtain all the mode components through empirical mode decomposition (EMD), denoise the mode components with noise using the wavelet threshold function to remove the noise components, select the optimal stratification for denoising the monitoring data of the Guozigou Bridge in Xinjiang in January 2023, determine the wavelet type and threshold selection criteria, and reconstruct the denoised intrinsic mode function (IMF) components to achieve accurate extraction of the effective signal. By referencing the deflection, temperature, and strain data of the Guozigou Bridge in Xinjiang in January 2023 and comparing the data cleaned by different mode decomposition and wavelet threshold denoising methods, the results show that compared with empirical mode decomposition (EMD)–wavelet threshold denoising and variational mode decomposition (VMD)–wavelet threshold denoising, the signal-to-noise ratios and root-mean-square errors of the four types of monitoring data obtained by the algorithm proposed in this study are the most ideal. Under the premise of minimizing reconstruction errors when processing a large amount of data, it has better convergence, verifying the practicality and reliability of the algorithm in the field of bridge health monitoring data cleaning and providing a certain reference value for further research in the field of signal processing. The computational method constructed in this study will provide theoretical support for data cleaning and analysis of nonstationary and nonlinear random signals, which is conducive to further promoting the improvement of bridge health monitoring systems.

Bridging the Gap: commodifying infrastructure spatial dynamics with crowdsourced smartphone data

Structural information deficits about aging bridges have led to several avoidable catastrophes in recent years. Data-driven methods for bridge vibration monitoring enable frequent, accurate structural assessments; however, the high costs of widespread deployments of these systems make important condition information a luxury for bridge owners. Smartphone-based monitoring is inexpensive and has produced structural information, i.e., modal frequencies, in crowdsensing applications. Even so, current methods cannot extract spatial vibration characteristics with uncontrolled datasets that are needed for damage identification. Here we present an extensive real-world study with crowdsourced smartphone-vehicle trips within motor vehicles in which we estimate absolute value mode shapes and simulate damage detection capabilities. Our method analyzes over 800 trips across four road bridges with main spans ranging from 30 to 1300 m in length, representing about one-quarter of bridges in the United States. We demonstrate a bridge health monitoring platform compatible with ride-sourcing data streams that check conditions daily. The result has the potential to commodify data-driven structural assessments globally.

Prestress Force and General Excitation Identification for Plate-like Concrete Bridges

Prestress force dominates the carrying capacity of concrete girders, and is vital for bridge health monitoring. Many vibrational-based methods have been proposed to determine prestress using bridge responses induced by known excitations, which means that they can barely use the normal traffic of in-service bridges as excitation to achieve long-term monitoring. Moreover, most studies are based on beam theory, which may not be precise for plate-like bridges. Hence, this paper establishes a motion equation for a prestressed slab via Kirchhoff’s plate theory and proposes a two-step procedure to assess the prestress and general excitation simultaneously through only bridge responses. The excitation is determined in the first step via the Load Shape function method and used as input for the prestress identification via state-space formulation in the second step. A numerical study on a prestressed plate subjected to a moving load is conducted. Considering different levels of measurement noise and load speed, the proposed method can determine the prestress and moving load with 7.33% and 10.18% error, respectively. A laboratory test on a prestressed box girder subjected to a fixed cyclic load is performed, the prestress and cyclic load are both determined to have good stability, and the errors are under 11.32%.

A three-year project on Structural Health Monitoring of railway bridges: main results and lessons learnt

Bridges and viaducts worldwide are threatened by ageing, increasing loads and climate-related extreme events. The situation calls for immediate actions to prevent catastrophic failures and extend the life of our infrastructural heritage. In this regard, thanks to significant efforts from academics, Structural Health Monitoring is paving its way towards application in the real world. Unlike the simple monitoring activity, which is not new in the field, SHM is more identifiable as a paradigm, encompassing several steps and involving many stakeholders. Also, the focus is not on a project level but on the network as a whole. Such a new perspective offers the opportunity – and obliges at the same time – to look for synergies among different bridges, with the aim of designing standardized guidelines to foster SHM scalability. Embracing this vision, in 2020, RFI and the Mechanical Engineering Department of Politecnico di Milano gave birth to a collaboration oriented to testing the application of Structural Health Monitoring on the Italian railway infrastructure. The project had a twofold objective: on the one hand, the realisation of an automated software to extract and deliver information from the monitoring systems installed on three pilot bridges; on the other hand, the draft of guidelines that could report the lessons learnt and generalise as much as possible the design and exploitation of SHM on railway bridges. Given the interest in the feasibility of an extensive adoption of such practice, the pilot bridges were selected to be representative of the whole network. This paper wraps up the three-year project, intending to share the experience gained in the field and the take-home messages for future applications of SHM, with a focus on the key aspects for a broad spectrum adoption of this paradigm.

Analyzing Vertical Earthquake Vibrations and Moving Vehicle Loads for Structural Health Monitoring and Vibration Suppression in Bridges

The design of bridges often overlooks the vertical component of earthquakes or considers it of secondary importance, despite compelling evidence indicating specific structural damage caused by primary earthquake waves. Conversely, during the operational phase, the combined influence of ground motion and moving loads from vehicles can significantly impact the structural health monitoring (SHM) of bridges. This study aims to evaluate the simultaneous effect of vertical earthquake vibrations and moving vehicle loads on simply supported bridges. The research employs a practical methodology based on the eigenfunction expansion method to analyze change of deflection due to the effect of these concurrent forces under seven different earthquake records. It is shown that within a realistic range of vehicle mass and velocity, the average of changing the maximum deflection at the mid-span of the main beam (denoted as M_n) reaches up to 163% under various scenarios. Subsequently, the seismic parameters influencing this phenomenon are identified through a statistical analysis of set of 100 different earthquake records with unique features. A linear regression equation is presented to predict the M_n based on the earthquake specific properties. Additionally, to control the vertical vibration of bridge systems, a novel vibration suppression system utilizing steel pipe dampers is introduced, and its reliability is examined across a broad spectrum of bridge flexural rigidity. The results indicate that the system's efficiency depends on M_n and the soil type of the bridge construction, enabling a reduction in structural sections (up to 27%) while achieving the same maximum target deflection in the initial state. This efficiency leads to a more economical design solution, emphasizing the potential benefits of the proposed system for practical application.

A dimensionless feature for continuous bridge health monitoring with tiltmeter measurements

Structural Health Monitoring (SHM) is an essential tool for bridges especially if they have a long period of activity. The bridge considered in this work allows the crossing of the river Po between Lombardia and Emilia-Romagna in Italy. It is about 1.2 km long and has 35 main spans: 25 simply supported beams and 10 cantilevers. The bridge, in use since 1958, underwent a closure period and extraordinary maintenance in 2019 due to severe structural problems. After reopening, the bridge is continuously monitored with a system of 88 bi-axial tiltmeters installed in May 2022. The sensors measure static inclinations (< 1Hz) providing information on the deflection and torsion of the bridge spans. The monitoring system returns a set of summary statistics for each inclination signal gathered in the last hour. Statistics like the mean are used to examine anomalous span drifts in the long term. Instead, the Interpercentile Range is used to evaluate span movements in the short term. In fact, the latter proved to be a good indicator of the hourly traffic load on the bridge, assuming a constant structure stiffness. Therefore, an SHM strategy is to monitor span movements, on a daily basis, using traffic as an active load. This makes it possible to identify any increases in bridge oscillations in relation to a reference condition, calculated over a given monitoring period. For this purpose, a dimensionless metric is proposed as monitoring feature. First, a theoretical analysis of metrics is presented. Then, results obtained from experimental bridge monitoring data are shown.

Human–Machine Collaboration Framework for Bridge Health Monitoring

Human–Machine Collaboration Framework for Bridge Health Monitoring

Mitigation of unmeasured environmental and operational variability in long-term bridge health monitoring by unsupervised statistical learning

Environmental and operational variations present a significant challenge in the long-term health monitoring of bridge structures due to their potential to cause serious confounding influences and wrong decisions. These influences may lead to false alarms of damage during normal operation of a civil structure or, conversely, mask actual damage, resulting in a failure to correctly signal a warning. Consequently, a critical task in structural health monitoring, particularly for bridges, is to mitigate the negative impact of environmental variability. An initial approach is to measure the most influential environmental and operational factors and then apply statistical tools to eliminate their effects. However, this approach may not be effective under all conditions. Therefore, the primary objective of this paper is to propose an innovative unsupervised statistical learning method that addresses the challenges posed by environmental and operational variability by considering unmeasured factors. The proposed method involves three steps: data clustering, unsupervised feature selection, and novelty detection. The initial step utilizes k-means clustering to categorize features (modal frequencies) into five types of clusters, reflecting the influence of five environmental and operational factors on bridge structures; that is, temperature, humidity/rain/snow, wind speed/direction, traffic, and damage. These clustered features are then processed through an unsupervised feature selection algorithm based on reconstruction independent component analysis to extract new features. These reduced features are subsequently employed in a novelty detection model, developed using the Mahalanobis-squared distance, to analyze the environmental and operational effects. Real dynamic data of a steel arch bridge is utilized to verify the proposed method. Results demonstrate that this method can effectively handle the demanding issue stemming from unmeasured environmental and operational variability conditions.

Enhancing Reinforced Concrete Bridge Health Monitoring: A Case Study on the Integration of InSAR, GPR, and LiDAR within 3D GIS Environment

Enhancing Reinforced Concrete Bridge Health Monitoring: A Case Study on the Integration of InSAR, GPR, and LiDAR within 3D GIS Environment

Jellyfish-inspired bistable piezoelectric-triboelectric hybrid generator for low-frequency vibration energy harvesting

In pursuit of enhancing the output power of energy harvesters, this paper introduces a novel jellyfish-inspired bistable piezoelectric-triboelectric hybrid generator (JIB-PTHG) with low potential energy barrier characteristics and low-frequency, broadband energy harvesting capabilities. The JIB-PTHG comprises two flexible beams, and two rigid links, connected via a spring. Utilizing the harmonic balance method and Runge-Kutta fourth-order algorithm, a comprehensive analysis of the mechanical properties of the low-barrier bistable structure is conducted. Static equilibrium bifurcation and dynamic response analysis are performed. Subsequently, an electromechanical coupling model incorporating piezoelectricity and triboelectricity is developed to theoretically predict the output voltage of the two modules, revealing a positive correlation between displacement and output voltage. Experiment validation demonstrated that adjusting the length of the rigid links can effectively increase the deformation of the piezoelectric beam and the relative displacement of the triboelectric layers, thus improving the generator's output performance. Specifically, the TENG module exhibited exceptional energy harvesting performance within the frequency range of 3.5–6.5 Hz, achieving a voltage of 1050 V, a power of 1.84 mW, and the ability to illuminate 70 LED lights under an excitation amplitude of 17.5 mm and a frequency of 5.5 Hz. The PEG module, displayed high output performance within the frequency range of 4-7 Hz, reaching a voltage of 6.2 Vs and a power of 12.8 μW at an excitation frequency of 7 Hz and an amplitude of 17.5 mm. Both theoretical and experimental findings indicate that the JIB-PTHG has achieved improved output power within the frequency range of 3.5–6.5 Hz. Furthermore, the JIB-PTHG has the potential to harness bridge vibration energy and convert it into useful electrical energy for powering wireless sensor nodes in bridge health monitoring systems.

Improved bridge modal identification from vibration measurements using a hybrid empirical Fourier decomposition

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.

Vibration-based SHM of railway steel arch bridge with orbit-shaped image and wavelet-integrated CNN classification

The study presents the data-driven applications of continuous wavelet transforms (CWT), orbit-shaped analysis and convolutional neural networks (CNN) using GoogLeNet for Dębica railway bridge health monitoring in Poland. Training and validation data sets are the dynamic behavior of the bridge deck recorded through an IEPE vibration sensor with a sampling frequency of 128 Hz from vibration-based structural health monitoring (SHM) system over a nine-month period from December 2019 to September 2020. Utilizing Morse, Morlet, and Bump wavelet, the vibration signal scalogram images are produced in the frequency–time domain as the input for CNN classification models, while the output is to predict health states based on the experimental tension force of eight hangers using label thresholds developed by calibrated finite element model. Moreover, the vibration-based orbit-shaped image patterns, acquired through a bidirectional sensor on each hanger are processed with CNN classification models for automated hanger health diagnostic. The accuracies and weighted F1-scores of the wavelet-assisted and orbit-shaped CNN models are more than 83% on imbalanced validation images. The results show that the proposed CNN approaches can classify the potential structural problems.

Research on Quantification of Structural Natural Frequency Uncertainty and Finite Element Model Updating Based on Gaussian Processes

During bridge service, material degradation and aging occur, affecting bridge functionality. Bridge health monitoring, crucial for detecting structural damage, includes finite element model modification as a key aspect. Current finite element-based model updating techniques are computationally intensive and lack practicality. Additionally, changes in loading and material property deterioration lead to parameter uncertainty in engineering structures. To enhance computational efficiency and accommodate parameter uncertainty, this study proposes a Gaussian process model-based approach for predicting structural natural frequencies and correcting finite element models. Taking a simply supported beam structure as an example, the elastic modulus and mass density of the structure are sampled by the Sobol sequence. Then, we map the collected samples to the corresponding physical space, substitute them into the finite element model, and calculate the first three natural frequencies of the model. A Gaussian surrogate model was established for the natural frequency of the structure. By analyzing the first three natural frequencies of the simply supported beam, the elastic modulus and mass density of the structure are corrected. The error between the corrected values of elastic modulus and mass density and the calculated values of the finite element model is very small. This study demonstrates that Gaussian process models can improve calculation efficiency, fulfilling the dual objectives of predicting structural natural frequencies and adjusting model parameters.