Bridge Structural Monitoring Research Articles

Toward Indirect Real-Time Prediction of Bridge Vibration Responses Under Traffic Flow Through a Population of Connected Sensing Vehicles

Condition monitoring of bridge structures as the lifelines of smart cities is high of importance. While indirect vehicle scanning techniques have shown promising results and have less cost compared to mounting fixed sensors on the bridge, they have limitations in predicting response under traffic flow since the crossing time of one vehicle is very short. This paper presents a novel crowsensing-based framework for predicting bridge acceleration responses and identifying the modal characteristics. This method utilizes smartphone data from a diverse population of sensing vehicles as they traverse the bridge to predict bridge acceleration response at various virtual sensing locations. Subsequently, the predicted acceleration is used to identify the mode shapes and natural frequencies of the bridge. The principal innovation in this practical and cost-effective monitoring solution is the utilization of a randomly selected set of sensing vehicles at each timestamp. These selections may differ from one timestamp to the next, reflecting the real-world conditions where certain vehicles may intermittently lose or disconnect their internet connectivity. By consistently updating the set of sensing agents while other vehicles cross the bridge, the proposed framework overcomes the data length limitations of conventional vehicle-based methods by leveraging multiple vehicles and continuous data collection. Comprehensive numerical studies are conducted to evaluate the performance of the method. In the numerical investigations, a three-span bridge is subjected to the continuous passing of a large number of half-car vehicle models with random speeds and initial locations. Vehicle-bridge interaction is considered in the analysis. Utilizing a single randomly selected sensing agent at each timestamp, the results demonstrate the effectiveness of the framework in predicting bridge acceleration response with a relative error of less than 5%. Additionally, the method achieves an accuracy level of 95% in identifying the bridge's initial three mode shapes.

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.

High-Precision Monitoring Method for Bridge Deformation Measurement and Error Analysis Based on Terrestrial Laser Scanning

In bridge structure monitoring and evaluation, deformation data serve as a crucial basis for assessing structural conditions. Different from discrete monitoring points, spatially continuous deformation modes provide a comprehensive understanding of deformation and potential information. Terrestrial laser scanning (TLS) is a three-dimensional deformation monitoring technique that has gained wide attention in recent years, demonstrating its potential in capturing structural deformation models. In this study, a TLS-based bridge deformation mode monitoring method is proposed, and a deformation mode calculation method combining sliding windows and surface fitting is developed, which is called the SWSF method for short. On the basis of the general characteristics of bridge structures, a deformation error model is established for the SWSF method, with a detailed quantitative analysis of each error component. The analysis results show that the deformation monitoring error of the SWSF method consists of four parts, which are related to the selection of the fitting function, the density of point clouds, the noise of point clouds, and the registration accuracy of point clouds. The error caused by point cloud noise is the main error component. Under the condition that the noise level of point clouds is determined, the calculation error of the SWSF method can be significantly reduced by increasing the number of points of point clouds in the sliding window. Then, deformation testing experiments were conducted under different measurement distances, proving that the proposed SWSF method can achieve a deformation monitoring accuracy of up to 0.1 mm. Finally, the proposed deformation mode monitoring method based on TLS and SWSF was tested on a railway bridge with a span of 65 m. The test results showed that in comparison with the commonly used total station method, the proposed method does not require any preset reflective markers, thereby improving the deformation monitoring accuracy from millimeter level to submillimeter level and transforming the discrete measurement point data form into spatially continuous deformation modes. Overall, this study introduces a new method for accurate deformation monitoring of bridges, demonstrating the significant potential for its application in health monitoring and damage diagnosis of bridge structures.

Performance monitoring and diagnosis of bridge structures based on time multiscale divide and conquer strategy

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.

Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data

Traditional bridge health monitoring methods that necessitate sensor installation are not only costly but also time-consuming. In contrast, utilizing smartphone data collected from vehicles as they traverse bridges offers an efficient and cost-effective alternative. This paper introduces a cutting-edge damage detection framework for indirect monitoring of bridge structures, leveraging a substantial volume of acceleration data collected from smartphones in vehicles passing over the bridge. Our innovative approach addresses the challenge of collecting and transmitting high-frequency data while preserving smartphone battery life and data plans through the integration of compressed sensing (CS) into the crowdsensing-based monitoring framework. CS employs random sampling and signal recovery from a significantly reduced number of samples compared to the requirements of the Nyquist–Shannon sampling theorem. In the proposed framework, acceleration signals from vehicles are initially acquired using smartphone sensors, undergo compression, and are then transmitted for signal reconstruction. Subsequently, feature extraction and dimensionality reduction are performed using Mel-frequency cepstral coefficients and principal component analysis. Damage indexes are computed based on the dissimilarity between probability distribution functions utilizing the Wasserstein distance metric. The efficacy of the proposed methodology in bridge monitoring has been substantiated through the utilization of numerical models and a lab-scale bridge. Furthermore, the feasibility of implementing the framework in a real-world application has been investigated, leveraging the smartphone data from 102 vehicle trips on the Golden Gate Bridge. The results demonstrate that damage detection using the reconstructed signals obtained through compressed sensing achieves comparable performance to that obtained with the original data sampled at the Nyquist measurement sampling rate. However, it is observed that to retain severity information within the signals for accurate damage severity identification, the compression level should be limited to 20%. These findings affirm that compressed sensing significantly reduces the data collection requirements for crowdsensing-based monitoring applications, without compromising the accuracy of damage detection while preserving essential damage-sensitive information within the dataset.

Damaging effects of earth fissures on high-speed railway bridges: Insights from physical experiments and numerical simulations

Earth fissure is a typical geological phenomenon related to surface rupture. With the rapid development of high-speed railways (HSRs) in recent years, earth fissure disasters pose a serious challenge to the construction and subsequent safe operation of HSRs. Here, we aimed to investigate the damaging effect of earth fissure activities on HSR bridges spanning the earth fissure zone. Taking the Shuixiu earth fissure of Da-Xi HSR as the prototype, a large-scale laboratory physical experiment with geometric model scale 1:20 was carried out to reveal the deformation and failure characteristics of HSR bridges under earth fissure activity. In addition, the damage influence range and damage mode of HSR bridges crossing earth fissures are further determined by numerical simulation. The results show that the earth fissure activity mainly has damaging effects on the structure of HSR bridges near the earth fissure zone, which is mainly manifested as uneven settlement of reinforced concrete pile, bending deformation of box girder and cracking of track slab. The width of the influence zone of Shuixiu earth fissure on the HSR bridge is determined to be 32.4 m. Moreover, the pile-end pressure on the hanging wall side of the earth fissure gradually decreases with the earth fissure activity, while that on the footwall side increases significantly. Most importantly, it is emphasized that a practical long-term monitoring and early warning system of HSR bridge structures should be established to prevent the hazards caused by earth fissure activities. The above understanding will benefit to the design, construction and operation of Da-Xi HSR line, and also provide significant guidance and reference to the construction of HSR bridges in other earth fissure prone areas.

Dynamic Monitoring of Bridge Structures via an Integrated Cloud and Edge Computing System

Dynamic Monitoring of Bridge Structures via an Integrated Cloud and Edge Computing System

Retracted: Development of Smart Sensing Technology Approaches in Structural Health Monitoring of Bridge Structures

Retracted: Development of Smart Sensing Technology Approaches in Structural Health Monitoring of Bridge Structures

Baseline-free structural damage detection using PCA- Hilbert transform with limited sensors

Conventional vibration-based damage identification methods need a large number of sensors on the bridge to identify the structural modal information and rely on the response data of bridges under a non-damage state for comparison, which restricts their implementations in the health monitoring of bridge structures in service. This paper proposes a baseline-free structural damage detection method based on limited sensor information. The proposed method uses the Hilbert transform combined with principal component analysis (PCA) to locate the damage. Firstly, PCA is performed on the displacement response data matrix of the bridge under a moving load to obtain the principal component matrix. According to the physical interpretation of PCA of the responses of a beam-like bridge under a moving load, each column of the principal component matrix, namely the principal component, composes of a mode shape and the dynamic information at this modal frequency of the bridge. The dynamic information of the modal frequency is indirectly obtained by filtering out the mode shape by low pass filter. Then the Hilbert transform is used to process the dynamic component information to receive the instantaneous frequency which is regarded as the damage index. When the structure is damaged, the first instantaneous frequency at the damaged location changes abruptly. To demonstrate the effectiveness and performance of the proposed method, numerical simulations and experimental validations on beam bridge models are conducted. The results show that the method is free from the requirement of the information of bridges under the undamaged state, and only limited sensors are needed to locate the damage.

Detecting changes in the structural behaviour of a laboratory bridge model using the contact-point response of a passing vehicle

ABSTRACT Drive-by bridge condition monitoring, using in-vehicle sensors to monitor bridges, represents a potential solution for network-scale monitoring of bridge structures. This paper presents a proof of concept for using the vehicle contact-point (CP) response for drive-by condition monitoring of bridges. An expression is presented which allows the vibration response at the point of contact between the tyre and the bridge surface to be inferred from the in-vehicle measurements. Following a simple numerical demonstration of the concept, laboratory tests are undertaken to verify that the CP response can be used to detect the fundamental frequency of the bridge. Results show that the CP response can be used to identify the bridge frequency with greater certainty than the signals measured directly on the vehicle. It is also shown, for two simulated damage cases, that changes in bridge frequency can be detected. The CP response is seen to be more sensitive to changes in bridge frequency than the measured signals. It is also observed that the detected frequency is sensitive to the vehicle speed and mass, which is an important consideration when combining results from multiple vehicle passages. Overall, the results verify that the CP response can be used to enhance drive-by bridge monitoring regimes.

A general data quality evaluation framework for dynamic response monitoring of long-span bridges

The structural health monitoring system (SHM) of long-span bridges inevitably produces low-quality data. It is important to evaluate the data quality and screen out normal data. Most existing studies on data anomaly detection have focused on abnormal data with abnormal waveforms in time domain. Usually there are pseudo-normal data in the SHM system, which seems normal in time domain. However, the pseudo-normal data of bridge dynamic responses may cause wrong dynamic characteristics identification. In this study, the existing data anomaly detection was further expanded to data quality evaluation, and both obvious abnormal and pseudo-normal data are evaluated. A general data quality evaluation framework for dynamic response monitoring of long-span bridge structures was proposed. The time–frequency characteristics of monitoring data were obtained by continuous wavelet transform, and the data quality was classified and evaluated by constructing and training a convolutional neural network (CNN). The proposed framework was verified by using acceleration monitoring data of a cable-stayed bridge. The results reveal that this framework solves the problem that CNN cannot detect pseudo-normal data through time-domain characteristics, and improves the accuracy of data quality evaluation by using time–frequency information of the monitoring data. The acceleration monitoring data of a suspension bridge and a single pylon cable-stayed bridge were used to demonstrate the feasibility of the cross-object application of the proposed framework. The evaluation accuracy for the acceleration data quality of the suspension bridge exceeded 96%. After the reinforced training of a small number of pseudo-normal data samples, the evaluation accuracy for the acceleration data quality of the cables of the cable-stayed bridge reached 96.8%. The framework shows a good generalization capacity and robustness in cross-object applications.

Bridge vibration energy harvesting for wireless IoT-based structural health monitoring systems: A review

Vibration energy has the advantage of abundant availability in the environment of the bridge structure due to consistent traffic flow and high-speed wind blowing. The vibration energy present at the bridge site can be harvested for powering the wireless sensors mounted on the bridge for health monitoring and making the system self-powered. The bridge vibrations are usually low frequency and low acceleration excitations, therefore, its transduction to useful electrical energy is a challenge. However, several techniques are being utilized to harvest the bridge vibration and a variety of bridge energy harvesters are being developed for this purpose. This work has reviewed energy harvesters claimed to be designed for bridge vibrations. The study is split into two categories, first section discusses the energy harvesters developed for bridge vibrations tested only in-lab environments. The second section is about the harvesters that have been characterized on real bridge structures to verify their effectiveness. The study reveals that the bridge energy harvesters can extract enough power to operate the wireless sensors for the health monitoring of bridge structures. Moreover, the architecture, fabrication, input excitation, and output performance of the reported harvesters with modeling techniques are discussed.

Feasibility evaluation of the development of type 1-3 acoustic emission sensors for health monitoring of large bridge structures

Feasibility evaluation of the development of type 1-3 acoustic emission sensors for health monitoring of large bridge structures

EET-Hamming monocular high-speed measurement for long-span bridge structure displacement on a shaking table

Monocular displacement measurement is widely used for the vibration monitoring of bridge structures on shaking tables. To efficiently and robustly measure the low-contrast targets obtained by a high-speed camera, this study proposes an EET-Hamming monocular high-speed measurement method. First, the EET and Otsu methods are combined to enhance and binarize the obtained image sequences, which can track the obtained low-contrast targets efficiently and robustly. Second, the minimum Hamming distance matching method is proposed to determine the similarity of the images and further improve the efficiency and accuracy of the image sequence processing. Finally, a nine-point least squares polynomial surface fitting method is proposed to calculate the sub-pixel displacement and convert them to the corresponding actual displacement. The results show that the proposed EET-Hamming monocular high-speed measurement method has a higher displacement measurement accuracy and processing efficiency.

Real-time drive-by bridge damage detection using deep auto-encoder

Structural health condition monitoring of bridge structures has been a concern in the last decades due to their aging and deterioration, in which the core task is damage detection. Recently, the drive-by method has gained much attention as it only needs several sensors installed on the passing vehicle. In this paper, we proposed an automatic damage detection method, which can be exploited in real time when the vehicle is passing the bridge. There are three steps in the proposed method: (1) The vehicle’s framed short-time vibrations instead of full-length data are utilized for training a deep auto-encoder model; at this stage, not commonly used time-domain accelerations of the passing vehicle, but its selected frequency-domain responses are employed to circumvent the influence of noises, (2) For the bridge with unknown health conditions, damage indicators can be extracted from its passing vehicle’s short-time vibration data using the trained model, and (3) The bridge’s health states are determined by real-time extracted damage indicators. To verify the proposed idea, a U-shaped continuous beam and a model truck are used to simulate the vehicle bridge interaction system in engineering. Results showed that the proposed method could identify the bridge’s damage with an accuracy of 86.2% when different severity was considered. In addition, it was observed that higher damage severity could not be revealed by greater values of damage indicators in the laboratory test. Instead, a novel index called identified damage ratios was employed as a reference for assessing the severity of the bridge’s damage. It was shown that with the increase in damage severity, the index would increase and gradually approach 100%.

Bridge damage detection using operating deflection shape ratios obtained from a passing vehicle

Large scale monitoring of bridge structures poses a significant challenge for infrastructure managers. This challenge could be overcome by using passing vehicles to monitor bridge condition. The operating deflection shapes of a bridge can theoretically be detected from in-vehicle measurements. This paper proposes a novel approach which introduces the concept of operating deflection shape ratios (ODSRs) from the responses measured on two following axles on a bridge. Unlike operating deflection shapes derived from drive-by measurements, ODSRs are non-dimensional values and are not a function of the bridge excitation level. This means that ODSRs are always constant for various vehicles passages. It is theoretically demonstrated in this study that ODSRs corresponding to each mode of bridge vibration can be approximately extracted from the responses of two following axles. Finite element modelling is employed to validate the feasibility of using ODSRs for bridge monitoring. The sensitivity of the ODSRs to damage is demonstrated numerically for damage at various locations, different vehicle speeds and different pavement roughness values. Laboratory experiments are then used to verify the theory in a practical environment. It is confirmed that the ODSR can be calculated from in-vehicle vibration measurements and can be used to detect changes in the structural behaviour of the bridge. The results of the study indicate that the ODSR represents a useful feature for drive-by bridge monitoring.

Research on bridge structure SAM based on real-time monitoring

To study the problems of frequent omissions and false alarms in the early warning system due to defects in the safety assessment method (SAM) of the existing bridge health monitoring systems (BHMS). In this paper, the current safety evaluation methods of BHMS are deeply studied through fuzzy comprehensive evaluation method and derive a new type of bridge health monitoring system SAM based on the safety evaluation vector. At the same time, combined with the concept of membership function, the result vector is accurately quantified, and a specific evaluation method for evaluating the safety level of the bridge structure is obtained. Combining the data of the three bridge health detection systems of 3 × 30 m continuous girder bridge, simply supported beam bridge, and steel–concrete composite girder bridge in the Beijing Metro Line 5 project, the daily and monthly safety assessment results of the three BHMS were verified. The research results show that the structural SAM proposed in this paper can accurately and in real-time evaluate the safety status of small and medium-span simply supported bridge structures. However, for the evaluation of continuous girder bridges and composite girder bridges, it is necessary to make specific judgments based on the actual conditions of the bridges. Moreover, the research in this paper can also consolidate the theoretical foundation for establishing the bridge structure monitoring data, early warning model. The research method in this paper can promote the further development of the bridge structural health monitoring system.

Dynamic Deformation Measurement of Bridge Structure Based on GB-MIMO Radar

Dynamic deformation measurement is an important approach to monitor the structure stability. Due to its rapid imaging capabilities, ground-based multi-input multi-output (GB-MIMO) radar has shown great application potential in bridge structure monitoring. This paper proposes a dynamic deformation estimation method based on radar image series. Firstly, an improved clutter suppression method is utilized to overcome the estimation error in complex working status. Secondly, a two-step pixel extraction method is adopted to ensure both quantity and quality of sample pixels. Finally, frequency- and time-domain environmental stimulating methods are jointly considered for stable mode shape estimation. This paper systematically measured the dynamic deformations of bridge structures, including two suspension bridges and a cable-stayed bridge. The time-series deformation, deflection, vibration frequency and amplitude on the bridge structure are obtained by image domain measurement. And for the first time, the mode shapes of bridges are obtained through GB-MIMO radar. The correctness of structure parameter measurement is verified with the finite element model (FEM). Experimental results prove that with the proposed method, stable results could be acquired against weak and complex stimulation conditions.

A Data-Driven Approach to Structural Health Monitoring of Bridge Structures Based on the Discrete Model and FFT-Deep Learning

A Data-Driven Approach to Structural Health Monitoring of Bridge Structures Based on the Discrete Model and FFT-Deep Learning

Modal frequencies of bridges from GNSS (GPS) monitoring data: Experimental, statistical evidence

GNSS technology (known especially for GPS satellites) for measurement of deflections has proved very efficient and useful in bridge structural monitoring, even for short stiff bridges, especially after the advent of 100 Hz GNSS sensors. Mode computation from dynamic deflections has been proposed as one of the applications of this technology. Apart from formal modal analyses with GNSS input, and from spectral analysis of controlled free attenuating oscillations, it has been argued that simple spectra of deflections can define more than one modal frequencies. To test this scenario, we analyzed 21 controlled excitation events from a certain bridge monitoring survey, focusing on lateral and vertical deflections, recorded both by GNSS and an accelerometer. These events contain a transient and a following oscillation, and they are preceded and followed by intervals of quiescence and ambient vibrations. Spectra for each event, for the lateral and the vertical axis of the bridge, and for and each instrument (GNSS, accelerometer) were computed, normalized to their maximum value, and printed one over the other, in order to produce a single composite spectrum for each of the four sets. In these four sets, there was also marked the true value of modal frequency, derived from free attenuating oscillations. It was found that for high SNR (signal-to-noise ratio) deflections, spectral peaks in both acceleration and displacement spectra differ by up to 0.3 Hz from the true value. For low SNR, defections spectra do not match the true frequency, but acceleration spectra provide a low-precision estimate of the true frequency. This is because various excitation effects (traffic, wind etc.) contribute with numerous peaks in a wide range of frequencies. Reliable estimates of modal frequencies can hence be derived from deflections spectra only if excitation frequencies (mostly traffic and wind) can be filtered along with most measurement noise, on the basis of additional data.