Bridge Health Monitoring System Research Articles

Mapping model between lateral responses of bridges under random traffic loads based on data-driven approach

Abstract The lateral distribution characteristic of a bridge is one of the important features reflecting its in-service state. Conventional deterministic indicators often struggle to consider this time-varying feature. In this study, a data-driven approach is employed to establish the mapping model among responses at the different lateral positions using the eXtreme Gradient Boosting model optimized by the Bayesian optimization algorithm. The proposed method is validated based on both data from a bridge health monitoring system equipped on an actual bridge and data from numerical simulation. At different lateral positions of the actual bridge, the overall coefficient of determination (R2) of the strain response mapped by the model is above 0.991, and the overall root mean squared error (RMSE) of the strain response mapped by the model are below 1.159 με. The numerical simulation method is used to consider a variety of working conditions with different road surface roughness and different traffic densities. Under different working conditions, the overall R2 of the deflection response mapped by the model is still above 0.961, and the overall RMSE of the deflection response mapped by the model is below 0.249 mm. These indicate that the proposed model can consider time-varying mapping relationships among responses at different lateral positions, and has good accuracy and applicability.

IoT Based Monitoring and Maintenance of Highway Bridges using WSN

This paper presents a WSN-based IOT-based bridge monitoring system. The automated real-time bridge health monitoring system has been made possible by sophisticated modifications to sensor technology; this system will aid in disaster management. The technology used in this development is a wireless sensor network (WSN). This system uses a variety of sensors to continually monitor the state of the bridge. These sensors include an accelerometer to identify jerks in the bridge, a vibration sensor to identify vibrations occurring on the bridge, a flex sensor to identify bends in the bridge, and a water level sensor to identify the water level. To determine the weight on the bridge, use the load sensor. The barrier is detected by an IR sensor. Only the buzzer will activate when these are high. Additionally, a microcontroller processes the data from many sensors and transmits it to a server and management system so that real-time bridge condition monitoring via a mobile device using the GSM model is possible

Smart Bridge Damage Assessment through Integrated Multi-Sensor Fusion Vehicle Monitoring

Abstract. This study explores the efficacy of vehicle-assisted monitoring for bridge damage assessment, emphasizing the integration of diverse sensor data sources. A novel method utilizing a deep neural network is proposed, enabling the fusion of fixed sensors on bridges and onboard vehicle sensors for damage assessment. The network offers scalability, robustness, and implementability, accommodating various measurement types while handling noise and dynamic loading conditions. The main novel aspect of our work is its ability to extract damage-sensitive features without signal preprocessing for future bridge health monitoring systems. Through numerical evaluations, considering realistic operational conditions, the proposed method demonstrates the capability to detect subtle damage under varying traffic conditions. Findings underscore the importance of integrating vehicle and bridge sensor data for reliable damage assessment, recommending strategies for optimal monitoring implementation by road authorities and bridge owners.

A bridge damage monitoring and assessment strategy based on Gaussian mixture modelling

For the bridges equipped with health monitoring systems, it is essential to precisely assess the health status of the bridge from the data with noise and outliers. An improved Gaussian mixture model clustering algorithm is used to process the obtained bridge strain sensor monitoring data and generate the characteristic data of clustering in order to improve the accuracy of data analysis of bridge health monitoring system. The cluster and feature data are obtained by Expectation Maximization process, and the isolated clusters are filtered out by the threshold parameters of weight and the Euclidean distance between clusters center. Based on the feature data, a scoring strategy is established to assess the bridge health and sensor status. The proposed strategy is used to analyze the monitoring data of a bridge in western China, and the analysis results show the availability of this strategy. Compared with the directly collected data, the processed bridge health scoring curve variation is smooth, which can reduce the influence of noise and abnormal data. The sensor status scoring curve can track changes in collected data and respond to different transformation scenarios. This shows that the proposed strategy can evaluate the state of this bridge and the sensors. provided a reference for bridge maintenance decision during the operational period.

Estimating Bridge Natural Frequencies Based on Modal Analysis of Vehicle-Bridge Synchronized Vibration Data.

This paper presents a method for accurately estimating the natural frequencies of bridges by simultaneously measuring the acceleration vibration data of vehicles and bridges and applying modal analysis theory. Vibration sensors synchronized with GPS timing were installed on both vehicles and bridges, achieving stable and high-precision time synchronization. This enabled the computation of the bridge's Frequency Response Functions (FRFs) for each mode, leading to a refined estimation of natural frequencies. The validity of the theory was confirmed through numerical simulations and experimental tests. The simulations confirmed its effectiveness, and similar trends were observed in actual bridge measurements. Consequently, this method significantly enhances the feasibility of bridge health monitoring systems. The proposed method is suitable for road bridges with spans ranging from short- to medium-span length, where the vehicle is capable of exciting the bridge.

A method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension

AbstractIn the actual structural health monitoring system of suspension bridges, only part of suspenders tension can be monitored, but not all the suspenders tension can be obtained. To solve this problem, a method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension is proposed. By using actual monitoring data of vehicle loads, a spatio‐temporal correlation model of the girder strain and tension forces of all suspenders is constructed based on the combined application of stacked denoising autoencoder and convolutional neural networks‐long short‐term memory model, so as to realize the preliminary identification of suspenders tension. On this basis, by using the actual monitoring data of suspenders tension and the strain monitoring data obtained through the distributed optical fiber sensors, the delicate identification of tension forces of all suspenders is realized based on the error interpolation of preliminary identification results. The results of the example bridge show that the method in this paper can effectively identify tension forces of all suspenders of the suspension bridge, and identification results are more accurate than the method using only the monitoring data of suspenders.

An IoT-Based Road Bridge Health Monitoring and Warning System.

Recent earthquakes worldwide have led to significant loss of life and structural damage to infrastructure, especially road bridges. Existing bridge monitoring systems have limitations, including restricted detection capabilities, subjectivity, human error, labor-intensive inspections, limited access to remote areas, and high costs. Aging infrastructures pose a critical concern for organizations and government funding policies, showing signs of decay and impending structural failure. To address these challenges, this research proposes an IoT-based bridge health status monitoring and warning system that is wireless, low-cost, durable, and user-friendly. The proposed system builds upon engineering standards and guidelines to classify bridge health status into categories ranging from excellent to collapse condition. It incorporates deflection, vibration, temperature, humidity, and infrared sensors, combined with IoT and a fuzzy logic algorithm. The primary objective is to reduce bridge maintenance costs, extend lifespans, and enhance transportation safety through an early warning system via a mobile application. Additionally, a Google Maps interface has been developed to display bridge conditions along with real-time traffic video. To validate the proposed system, a 3-D prototype model was constructed and tested. Practical testing of the fuzzy logic algorithm aligned with the simulation outcomes, demonstrating expected accuracy in determining bridge health status.

Harvest more bridge vibration energy by nonlinear multi-stable piezomagnetoelastic harvester

The idea of using bridge vibration energy to power node sensors is promising in solving the power problems of bridge health monitoring systems. This study proposes a piezomagnetoelastic harvester that tries utilizing nonlinear multi-stability to improve the harvesting performance of bridge vibration energy. The harvester can exhibit multi-stable behaviors due to the introduced nonlinear forces. The potential energy functions are derived and corresponding analyzes are carried out. The results show that the tri-stable state of harvester can be obtained by adjusting the parameters related to magnetic forces. Its potential barriers are low and the distance between potential wells is large, so it can execute snap-through motion easily. The experimental study was carried out for different vehicle speeds. The results verify that the energy harvester in a tri-stable state can generate the largest electric output over a wide range of vehicle speeds. Moreover, it is proved that the tri-stable state can protect the harvester from excessive deformation and subsequent damage. Therefore, the tri-stable configuration has a potential advantage in bridge vibration energy harvesting and providing power for self-powered sensors.

Integration of Bridge Health Monitoring System With Augmented Reality Application Developed Using 3D Game Engine–Case Study

Integration of Bridge Health Monitoring System With Augmented Reality Application Developed Using 3D Game Engine–Case Study

Forecasting and early warning of bridge monitoring information based on a multivariate time series ARDL model

For bridge monitoring data, employing time series analysis to establish mathematical models that can reflect the operational state of a structure and predict its future state is a feasible research approach for bridge condition assessment. Existing studies primarily consider data from a single monitoring point. However, the bridge health monitoring system (HMS) is equipped with various sensors, and the monitoring data obtained from different measuring points are not isolated. Therefore, this study aims to utilize a multi-point, multi-variable perspective. The multivariate ARDL model, which utilizes multiple monitoring time series, is developed and the interrelationships between different measuring points and variables are investigated. Moreover, a method for structural forecasting and early warning that utilizes the ARDL model is developed. For the warning threshold interval, the confidence interval of the predicted values is utilized, and the establishment and dynamic updating of the periodic model is discussed. Finally, based on the health monitoring data of Kunshan Yufeng Bridge, the ARDL model for bearing displacement and temperature monitoring data is established. Using the model, this study explores the long-term and short-term quantitative relationships and the interactions between bearing displacement and temperature variables. Subsequently, forecasting and early-warning analyses are conducted. Compared with the ARIMA model, which is based on a single displacement variable, the forecasting MAE, RMSE, MAPE, and TIC of the ARIMA model are 2.38681, 3.34296, 0.08698, and 0.06215, while these metrics of the ARDL model are only 0.53197, 0.62786, 0.01913, and 0.01138. Apparently, the ARDL model exhibits much higher forecasting accuracy; thus, early warning precision is ensured. The early-warning and assessment method established herein is robust against bridge types or monitoring parameters; thus, it exhibits satisfactory applicability and feasibility.

Analysis of common diseases, monitoring, and maintenance of cable-stayed bridges

The tide of bridge building has passed. Nowadays, the monitoring and maintenance of bridges is very important for their safety, durability, and applicability, and it is the focus of the worlds attention and research. This paper analyzes the common diseases and maintenance strategies of common cable-stayed bridges and cable-stayed bridges in coastal areas. This paper includes system composition, sensor layout, sensor types, monitoring objects, structural health status evaluation, and daily maintenance, as well as Sutong Bridge, Changmen Grand Bridge, and Ganjiang Grand Bridge of Changgan Passenger Transport in Ganzhou and other concrete examples. The modules and functions of structural health status assessment and the application of the bridge health monitoring system, such as early warning and assessment in the daily monitoring of cable-stayed bridges, are introduced. And Changmen Grand Bridge, Ganjiang Grand Bridge of Changgan Passenger Transport in Ganzhou, and Sutong Bridge are compared and analyzed according to the arrangement of measuring points, monitoring objects, and corresponding sensors. Finally, this paper briefly discusses the basic methods of preventive maintenance, BIM, and other related intelligent monitoring methods.

Research on structural health monitoring methods for bridge structures

With the development of science and technology, China is building more and more Bridges. The inspection and maintenance of bridge during operation plays an important role in the safety of bridge. Firstly, the research background and significance of bridge structure health inspection are introduced, and the research status and progress of health inspection are analyzed. Then, several commonly used monitoring methods are listed. Based on the comprehensive study of the bridge health monitoring system, the main conclusions are as follows: the bridge health monitoring system is realized through the coordination of several subsystems, including: bridge working environment monitoring, bridge structure overall performance monitoring and bridge structure local performance monitoring. The bridge structural health monitoring system is mainly used in bridge damage identification and bridge safety warning. The bridge structure health monitoring system conforms to the development trend of high technology and will become an indispensable part of the bridge operation in the future.

Singular value decomposition and unsupervised machine learning for virtual strain sensing: Application to an operational railway bridge

Metal fatigue is a major concern in civil, mechanical, and offshore structures. As a result, inspections are frequently directed to critical, fatigue prone locations using visual inspections, nondestructive evaluations, or sensor data. However, fatigue assessment may be hampered if access to these fatigue-prone areas is difficult or impossible. Furthermore, when comprehensive sensor use as part of a bridge health monitoring system is desired, cost and power requirements can be prohibitive. These identified limitations have motivated studies examining virtual sensing methods that estimate strains at unmeasured locations using indirect measurements. Kalman filtering (KF) and modal expansion (ME) are two popular strain estimation processes. However, both processes are model-based, requiring calibration of a finite element (FE) model of the structure of interest, which can be time-consuming, particularly for complex structures. This constraint has spurred the need for data-driven strain estimation methods, which depends solely on data from the structure, typically provided by sensors, without any additional a priori knowledge of the structure, such as what could be provided by a FE model. This study investigated the use of a novel, data-driven, Singular Value Decomposition (SVD) based method for strain estimation on an operational railroad bridge. Left singular vector (LSV) SVD modes, also known as Proper Orthogonal Modes (POMs), were employed for strain estimation. Machine learning (ML) was implemented to reduce POM variability and subsequently increase estimation accuracy using two classification methods: k-means clustering and root mean square (RMS); and self-organizing maps (SOM) and POMs. Strains were predicted using strain time-history POMs from snapshot matrices from clustered groups of train passages to estimate unmeasured time-histories from the same group. The method was applied to operational strain measurements from approximately 300 train passages over a steel, truss, railway bridge. Results showed that use of the data driven SVD technique in conjunction with ML could predict suitable strain time signals at unmeasured locations, data that can subsequently be utilized when performing fatigue assessment.

Research on mapping relationship between environmental load and vibration response of bridge structure based onstructural health monitoringdata

AbstractWith the increasing lifespan of bridges, various forms of deterioration will inevitably occur. The bridge health monitoring system enables direct monitoring of the loads (such as wind, temperature, and vehicles) experienced by the bridge during operation, as well as the corresponding vibration response of the bridge structure (including main beam deformation, tower top displacement, cable acceleration, etc.). These data provide valuable insights into the safety and durability of the bridge during its operational lifetime. However, the challenge lies in extracting meaningful information from the vast amount of collected data. This article focuses on the Fuyu Bridge as a research case and analyzes the variations in environmental factors, such as temperature, humidity, wind load, and structural vibration responses (main beam and cable), obtained from the Fuyu Bridge's structural health monitoring system. It also conducts a structural safety assessment during the operational period of the bridge. Furthermore, the research explores the relationship between environmental factors, load effects, and structural response to establish a foundation for future structural safety assessments of the Fuyu Bridge during its operational period. For instance, changes in environmental factors or load effects can be used to predict structural responses. Additionally, this study provides access to the structural health monitoring data of the Fuyu Bridge, facilitating data‐driven research for other scholars in the field.

Study on damage identification of High-Speed railway truss bridge based on statistical steady-state strain characteristic function

The localization and quantification of damage in bridge structures have always been a challenging problem, especially for high-speed railway truss bridges with intricate structural forms and complex load conditions. To address this issue, this paper proposes a novel damage indicator based on strain characteristic functions, and integrated it into an automated framework for real-time damage detection which is particularly suitable for in-service structural health monitoring applications. To evaluate the effectiveness of this approach, a study is conducted using a finite element digital twin model to simulate the structural responses at critical areas of high-speed railway truss bridges under train-induced loads. The proposed damage indicator of the model was used to investigate and evaluate different types of bridge damage, reflecting both the location and severity of damage in high-speed railway truss bridges. The results suggest that the proposed damage indicator, when used with a distributed sensor system, shows promise in accurately locating and measuring damage in high-speed railway truss bridges. In addition, for the bridge health monitoring system with sparse sensor distribution, this indicator can provide a probability assessment of various damage conditions of a high-speed railway truss bridge. This information could potentially assist in the process of conducting in-service maintenance of a high-speed railway truss bridge.

A case study of processing abnormal GNSS monitoring data of a cable-stayed bridge in Vietnam

Global Navigation Satellite System (GNSS) plays an important role in the real-time monitoring of superstructures, especially for long-span cable-stayed bridges. Currently, GNSS has been applied to integrate a Structural Health Monitoring system (SHMs) of many long-span cable-stayed bridges worldwide through its advantages in observing a large displacement of structures and monitoring the global deformation of bridges. However, some studies of actual GNSS monitoring data of a cable-stayed bridge showed that there are a lot of abnormal data occurrences such as missing data or several abnormal data. This paper investigates the application of some methods for processing the GNSS abnormal data acquired from an actual cable-stayed bridge in Vietnam. Firstly, a long-term monitoring dataset of an actual bridge was acquired for the study experiment. A clean-short dataset was used to investigate the accuracy and applicability of some methods in the interpolation of abnormal data, for example, linear formula, Moving Average, Artificial Neural Network, and Hampel Identifier. Some criteria are used to assess the difference between the interpolated data and actual data such as Root Mean Square Error (RMSE) or correlated coefficients with temperature data. Otherwise, the applied methods were then assessed their abilities and effectiveness in real applications, for instance, creating an automatic interpolated program of abnormal data.

Study on strain characteristic function for performance evaluation of high-speed railway steel truss bridge

From the data collected by bridge health monitoring systems, various indicators can be derived to evaluate the structural, environmental, and loading conditions. These indicators provide a basis for decision-making of bridge operations managers and administrators. In this paper, a new monitoring indicator called the Statistical Steady-State Static Effect Characteristic Function is proposed for railway bridges. A digital twin (DT) finite element model is developed in this study, which allows for real-time analysis of the structural effects during train travel. Using this model, the strain effects on key components of a high-speed railway steel truss bridge under dynamic loading conditions caused by random trains are simulated. Based on the characteristics of the simulation results, an extraction algorithm for the statistical steady-state strain characteristic function of the high-speed railway steel truss bridge is proposed in this study. By simulating the strain effects under multiple random train travel events, the strain characteristic functions at different locations of the structure are extracted, demonstrating the applicability of the proposed algorithm. Finally, by comparing these strain characteristic functions, the distribution pattern in the structural configuration space is revealed, suggesting the potential of the function as an indicator for assessing inherent structural characteristics, monitoring structural condition, and identifying damage.

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.

Vertical and Horizontal Combined Algorithm for Missing Data Imputation in Bridge Health Monitoring System

Structural health monitoring (SHM) provides strong support for bridge safety evaluation by tracking the real-time structural performance. An accurate and efficient imputation method for missing data in the SHM system is of vital importance for bridge management. In this paper, an innovative vertical–horizontal combined (VHC) algorithm is proposed to estimate the missing SHM data by a more comprehensive consideration of different types of information reflected in different time dimensions of the monitoring data. In the vertical time dimension, the variation trends and changing ranges for the missing data are analyzed by the relational analysis from the global view of data. In the horizontal time dimension, the exact value of the missing data is estimated based on the adjacent data inside the missing data set from the local view of data. The proposed VHC algorithm is validated and compared with the current methods in the data imputation for the missing deflection values in the SHM system of an example tie-arch bridge. The results show that the proposed algorithm demonstrates the best imputation performance among all methods for both missing types with the highest imputation accuracy/similarity and satisfying the 95% confidence bound. The application of reward coefficient significantly increases the imputation accuracy for the continuous missing situation and the improvement grows with the missing step number. Loss ratio and sample size also have significant influences on the imputation performance with the proposed method.

The Current Development of Structural Health Monitoring for Bridges: A Review

The health monitoring system of a bridge is an important guarantee for the safe operation of the bridge and has always been a research hotspot in the field of civil engineering. This paper reviews the latest progressions in bridge health monitoring over the past five years. This paper is organized according to the various links of the bridge health monitoring system. Firstly, the literature on monitoring technology is divided into two categories, sensor technology and computer vision technology, for review. Secondly, based on the obtained monitoring data, the data processing methods including preprocessing, noise reduction, and reconstruction are summarized. Then, the technical literature on abnormal data early warning systems is summarized. The recent advances in vibration-based and non-destructive testing-based damage identification methods are reviewed in the next section. Finally, the advantages and disadvantages of the existing research and the future research directions are summarized. This review aims to provide a clear framework and some reliable methods for future research.