Bridge Structural Health Monitoring Research Articles

A spatiotemporal distribution identification method of vehicle weights on bridges by integrating traffic video and toll station data

Real-time monitoring of the spatiotemporal distribution of vehicle weights on bridge decks is an important component of bridge structural health monitoring systems. However, it is still a challenge to identify the spatiotemporal distribution of vehicle weights on the whole bridge deck because the existing identification techniques are based on the theory of line of influence or need to install a weight-in-motion (WIM) system on the bridge. This paper proposes an information fusion-based identification method for the spatiotemporal distribution of vehicle weights without WIM installation, in which, (1) the traffic videos acquired by multiple cameras arranged along both sides of the bridge are used to detect the spatiotemporal distribution and license plate of the vehicles, and the weights obtained from the toll station are linked to the vehicles by matching the license plates. In addition, (2) a digital image correlation (DIC)-based vehicle tracking method is proposed to solve the problems of frame drop and missing detection and (3) a polynomial fitting-based coordinate transformation method is proposed to avoid the derivation of complicated coordinate conversion formula related to pinhole camera. The efficiency and accuracy of the proposed identification approach are verified by the field data collected from a cable-stayed bridge and nearby toll stations. The results indicate that our proposed method is a feasible and reliable solution for identifying spatiotemporal distribution of vehicle weights on bridges.

Reference-Free Vibration-Based Damage Identification Techniques for Bridge Structural Health Monitoring—A Critical Review and Perspective

Reference-Free Vibration-Based Damage Identification Techniques for Bridge Structural Health Monitoring—A Critical Review and Perspective

Computer Vision-Based Real-Time Identification of Vehicle Loads for Structural Health Monitoring of Bridges

Coupling effects of various loading conditions can cause deflections, settlements and even failure of in-service bridges. Although it is one of the most critical loads, unfortunately, loading conditions of moving vehicles are difficult to capture in real time by bridge monitoring systems currently in place for sustainable operation. To fully understand the status of a bridge, it is essential to obtain instantaneous vehicle load distributions in a dynamic traffic environment. Although there are some methods that can identify overweight vehicles, the captured vehicle-related information is scattered and incomplete and thus cannot support effective bridge structural health monitoring (BSHM). This study proposes a noncontact, vision-based approach to identification of vehicle loads for real-time monitoring of bridge structural health. The proposed method consists of four major steps: (1) establish a dual-object detection model for vehicles using YOLOv7, (2) develop a hybrid coordinate transformation model on a bridge desk, (3) develop a multiobject tracking model for real-time trajectory monitoring of moving vehicles, and (4) establish a decision-level fusion model for fusing data on vehicle loads and positions. The proposed method effectively visualizes the 3D spatiotemporal vehicular-load distribution with low delay at a speed of over 30FPS. The results show that the hybrid coordinate transformation ensures that the vehicle position error is within 1 m, a 5-fold reduction compared with the traditional method. Wheelbase is calculated through dual-object detection and transformation and is as the primary reference for vehicle position correction. The trajectory and real-time speed of vehicles are preserved, and the smoothed speed error is under 5.7%, compared with the speed measured by sensors. The authors envision that the proposed method could constitute a new approach for conducting real-time SHM of in-service bridges.

BPF-WT combined filtering method for indirect identification of bridge dynamic characteristics

The indirect measurement method of extracting the dynamic characteristics of the bridge structure from the dynamic response of passing vehicles is a new method for bridge structure health monitoring, which has the advantage of cost-effectiveness. However, how to improve the accuracy of the indirect measurement method to identify the dynamic characteristics of the bridge is the current research focus. In this paper, combining the characteristics and advantages of bandpass filter (BPF) and wavelet transform (WT), a simple and effective joint filtering method BPF-WT is proposed for indirect identification of bridge dynamic characteristics. This method can eliminate the peak caused by the known vehicle frequency, and realize the layer-by-layer decomposition and reconstruction of the signal through the advantage of WT, to achieve the purpose of automatically executing the packet filtering task. This paper takes a simple trolley as an example to conduct a scaled test study to identify the dynamic characteristics of a bridge and proves that the BPF-WT joint filtering method can perform bridge dynamics under three conditions: different vehicle weights, different driving speeds, and driving on bridge decks with different roughness. Characteristic identification results and their influence rules. According to the research findings, the method of combined filtering suggested in this paper can efficiently filter out the noise produced by the vehicle’s frequency and the surroundings. This method can also distinctly detect the initial three frequencies of the bridge. Therefore, it demonstrates the practicality and efficiency of the BPF-WT combined filtering technique, which has a broad range of potential applications.

Optimal sensor placement for bridge structural health monitoring: Integration of physics-based models with data-driven approaches

Optimization of sensor configurations for cost-efficient structural health monitoring (SHM) plays a pivotal role in ensuring the safety and longevity of critical infrastructures such as bridges while minimizing maintenance expenses. Infrastructure systems may evolve over time, and their monitoring needs may change. Hence, the best sensor configuration must be designed to be scalable and adaptable to future requirements without major overhauls.The present work explores this optimization problem as an essential step of the development and implementation of a robust digital twinning strategy, by combining the strengths of physics-based models with data-driven insights. Conventional methods for sensor placement typically rely on expert knowledge, oftentimes resulting in suboptimal configurations and excessive installation costs. Conversely, physics-based models provide a rigorous comprehension of the structural behavior and can inform sensor placement procedures by assessing the impact of sensor locations on the monitoring accuracy. However, these models have limitations, such as simplifying assumptions, uncertainties, and computational complexities. To overcome these issues and upgrade the sensor placement process, data-driven approaches can be integrated to extract patterns, correlations, and unforeseen anomalies.To demonstrate the effectiveness and benefits of this hybrid framework, a case study of an existing bridge instrumented with sensors is presented. First, a simplified physics-based model is developed and calibrated through real-world data obtained from an extensive dynamic identification campaign comprising 108 instrumented degrees of freedom. Then, a reduced number of sensors are selected through a data-driven optimization strategy as best candidates for the deployment of a long-term monitoring system. Finally, the virtual model is employed to simulate varying damage scenarios and validate whether the sensor location experimentally identified as optimal would remain the best even when structural conditions change. The ultimate goal is to foster proactive maintenance strategies by striking a balance between data quality, sensor coverage, and SHM cost.

Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

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

Integrated Bridge Structural Health Monitoring System

Integrated Bridge Structural Health Monitoring System

Advancing Bridge Structural Health Monitoring: Insights into Knowledge-Driven and Data-Driven Approaches

Structural health monitoring (SHM) is increasingly being used in the field of bridge engineering, and the technology for monitoring bridges has undergone a radical change. It has evolved from the initial local monitoring and assessment, which relied mainly on manual work, to the current all-round and full-time intelligent assessment provided by intelligent monitoring systems. This paper reviews the development of SHM technology in the civil engineering field and examines two current artificial intelligence (AI) methods in bridge SHM, namely knowledge-driven and data-driven approaches. The advantages and disadvantages of these two AI methods are analyzed, and future development trends are also discussed. The overview results reveal that knowledge-driven methods have the advantages of interpretability and stability. However, their current application is limited, and significant technical bottlenecks remain. On the other hand, the data-driven approach demonstrates higher efficiency and accuracy. Nevertheless, it is characterized by instability and insecurity due to its "black-box" nature, which hinders its ability to explain the internal operation mechanism. Given these findings, the hybrid knowledge-data-driven approach emerges as a potential solution. This approach can effectively integrate the advantages of both knowledge-driven and data-driven methods while avoiding their respective disadvantages. Consequently, the hybrid approach proves to be more stable, safe, and efficient in practical applications.

Digital Twin Framework for Bridge Structural Health Monitoring Utilizing Existing Technologies: New Paradigm for Enhanced Management, Operation, and Maintenance

Bridges are one of the most important elements of a sustainable transportation infrastructure network and require significant care to guarantee they function in a safe and reliable manner. Ensuring effective bridge maintenance and life cycle management in the face of aging and deterioration requires precise performance data and efficient assessment technologies. Because of the overall mediocre quality of U.S. infrastructure, there is a crucial need to enhance its monitoring and management. Digital twin is an emerging and promising concept that can offer the required solution. Advances in sensors, computing, communications, and predictive data analytics, coupled with biannual inspections, offer a wealth of bridge assessment data that will need a centralized digital twin framework for integration, analysis, and optimal decision-making with regard to maintenance. Leveraging and integrating these technologies in the new paradigm of a bridge digital twin will provide state transportation agencies with additional resources not previously available to enhance bridge operation and maintenance. A major value of a digital twin is the new knowledge created by the integration of information resources. This paper provides a framework for bridge digital twin that incorporates existing technologies, namely, building information modeling, structural health monitoring, intelligent transportation systems, and geographic information systems. The paper also provides an overview of practical applications and use cases of digital twin for bridges and structures, and offers an assessment of the challenges posed by the technology.

Neural Network-Based Anomaly Data Classification and Localization in Bridge Structural Health Monitoring

Due to the harsh working environments of certain bridges, the bridge structural health monitoring systems (SHMs) are prone to error warning because of anomaly data. Therefore, it is of great significance to accurately classify and locate the anomaly data for effectively addressing these issues. This paper proposes a method for classifying and locating anomaly data utilizing one-dimensional monitoring data based on convolutional neural networks. Compared to previous research reliant on visual features, the proposed method has lower computational costs. By incorporating the anomaly data localization network, manual localization operations for data restoration are replaced. The analysis in this paper is based on monitoring data from a large-span cable-stayed bridge, along with artificially generated anomaly data. The two neural network frameworks proposed in this paper are trained and validated, showcasing precise classification and localization of anomaly data. Furthermore, the paper discusses the impact of common errors in labeling data categories and locating training samples in practical operations. The results demonstrate that even in the presence of noticeable yet non-extreme labeling errors in the training set, the proposed method still achieves accurate classification and localization, highlighting its robustness.

Sampling methods based on expected traffic-volume information for long-term rotation-based bridge SHM in resource-constrained environments

Effective bridge structural health monitoring systems offer a route to assist in the safe and economic operation of bridges, but have seen limited adoption onto the short and medium-span bridges which represent the majority of our bridge stock. This lack of adoption is in part due to the practical challenges of deploying bridge SHM systems in resource-constrained environments, especially where long-term monitoring at transport network-scales is considered. This paper tackles the problem of implementing an effective sampling method to facilitate long-term, bridge rotation-based damage detection, particularly within the confines of a resource-constrained setting. We demonstrate that leveraging readily available expected traffic-volume information enables the creation of more effective bridge SHM sampling methods by capturing more vehicle crossings in the same time. Furthermore, we introduce an empirically tuned, temperature-capable bridge finite-element model for long-term simulations, incorporating a stochastic vehicle arrival model and a feature error model to allow for the presence of any residual noise/dynamics due to imperfect filtering or inaccuracies in the feature extraction stages. This allowed us to simulate 60 years worth of bridge rotation monitoring data with varying damage severities to evaluate the impact of environmental and operational variations on our proposed expected traffic volume based sampling methods. Our results show that sampling only the peak traffic hours was the most effective for the same reduced ‘on‘ time, i.e. for an 8x reduction in ‘on‘ time only a 1.5x increase in the minimum detectable damage was observed versus a 2x increase for naive random sampling.

Self‐Powered Intelligent Damper Integrated Triboelectric‐Electromagnetic Hybrid Unit for Vibration In Situ Monitoring of Stay Cables

AbstractMonitoring 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.

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.

Bayesian Vehicle Load Estimation, Vehicle Position Tracking, and Structural Identification for Bridges with Strain Measurement

Vehicle load estimation and health monitoring of bridges are of great importance for the health monitoring of bridge structure under vehicle loads. Traditional methods for the estimation of vehicle load require the positions of the vehicles. The vehicle position tracking is generally conducted in offline manner and requires the installation of additional sensors. To resolve these problems, we developed a Bayesian probabilistic approach for the online estimation of vehicle loads, vehicle positions, and structural parameters for bridges. The crux is to model the vehicle load vector as a modulated filtered Gaussian white noise due to the fact that the vehicle-bridge interaction forces are in essence the responses of the vehicle-bridge coupled system under the excitation of the road roughness described by Gaussian random field and the constant vehicle weights. Furthermore, the vehicle speed vector is introduced to track the unknown positions of vehicles. There are three appealing features in this approach. First, it allows the simultaneous estimation of vehicle loads, vehicle positions, and structural parameters in an online manner. Second, this method allows for time-varying vehicle speed tracking. Third, the proposed method is applicable to the case with multiple vehicles. Examples for the case where single/multiple vehicles pass across bridges with uniform speeds/variable speeds are presented to demonstrate the feasibility of the proposed method for vehicle load estimation, vehicle position tracking, and bridge structural identification using only strain measurements.

Identification of Moving Vehicle Loads Using Instantaneous Vision-Based Vehicle Spatiotemporal Information and Improved Time Domain Method

Accurate identification of moving vehicle loads on bridges is one of the challenging tasks in bridge structural health monitoring, but lacks of intensive investigations to merge the heterogeneous data of vision-based vehicle spatiotemporal information (VVSI) and vehicle-induced bridge responses for moving force identification (MFI) in the existing time domain methods (TDM). In this study, a novel MFI method is proposed by integrating instantaneous VVSI and an improved TDM (iTDM). At first, a novel VVSI method combining background subtraction with template matching is presented to accurately track moving vehicles on bridges. With the calibration technique and camera perspective transformation model, the distribution of vehicles (DOV) on bridges is obtained and used as a priori information in the subsequent MFI. Then, the iTDM is developed based on the MFI equation re-formed in the form of instantaneous VVSI instead of the constant speed vehicle crossing bridges assumed in the traditional TDM. Finally, based on the redundant dictionary matrix composed of Haar functions for a moving load, the MFI problem is converted to explore a solution to the atom vectors and then solved by the Tikhonov regularization method. Experimental verifications in laboratory and a comparative study with the existing three methods are conducted to assess the feasibility of the proposed method. The results show that the proposed MFI method outperforms the existing methods and can effectively identify the moving vehicle loads with a higher and acceptable accuracy. It is successful for the proposed method to replace the assumption of constant speed vehicle crossing bridge in the traditional TDM with the instantaneous VVSI in the MFI problem.

A method to maximise the information obtained from low signal-to-noise acceleration data by optimising SSI-COV input parameters

Structural Health Monitoring (SHM) has mainly been undertaken on larger bridges and on a case-by-case basis. This is due to a range of factors, such as the high installation costs and the effort required to install and commission the monitoring systems. One way in which SHM systems can become more feasible for widespread adoption at a network level is to reduce the number and cost of sensors used. However, this comes with a trade-off as low-cost sensors will typically have a worse signal-to-noise ratio and the reduced number of sensors requires careful placement to maximise the amount of information acquired. One of the simplest/cheapest methods of bridge SHM is long-term tracking of the bridge frequency to identify a change in stiffness. Using data collected from five in-service bridges, this research shows that the user-defined SSI-COV input parameters can significantly impact the quality of extracted natural frequencies. Consequently, a novel method is developed to aid in choosing the inputs used in the SSI-COV method. The method developed also showed that the determined inputs resulted in the extraction of accurate natural frequencies with minimal apparent outliers on all tested bridges. The developed method allows the extraction of quality natural frequencies from low signal-to-noise acceleration data which are vital when undertaking frequency-based SHM.

Towards the Structural Health Monitoring of Bridges Using Wireless Sensor Networks: A Systematic Study.

To perform a comprehensive assessment of important infrastructures (like bridges), the process of structural health monitoring (SHM) is employed. The development and implementation of SHM systems are generally based on wireless sensor networks (WSN) platforms. However, most of the WSN platforms are battery-powered, and therefore, have a limited battery lifetime. The power constraint is generally addressed by applying energy harvesting (EH) technologies. As a result, there exists a plethora of WSN platforms and EH techniques. The employment of a particular platform and technique are important factors during the development and implementation of SHM systems and depend upon various operating conditions. Therefore, there is a need to perform a systematic literature review (SLR) for WSN platforms and EH techniques in the context of SHM for bridges. Although state-of-the-art review articles present multiple angles of the field, there is a lack of an SLR presenting an in-depth comparative study of different WSN platforms and EH techniques. Moreover, a systematic analysis is also needed for the exploration of other design considerations such as inspection scale (global/local), response type (static/dynamic), and types of sensors. As a result, this SLR selects 46 articles (during 2007-2023), related to EH techniques and WSN platforms in SHM for bridges. The selected articles are classified into three groups: WSN platforms, energy harvesting techniques, and a combination of both. Subsequently, a comparative analysis of WSN platforms and EH techniques is made. Furthermore, the selected articles (total = 46) are also explored in terms of sensor type, inspection scale, and response type. As a result, 17 different sensor types are identified. This research is significant as it may facilitate the various stakeholders of the domain during the selection of appropriate WSN platforms, EH techniques, and related design issues.

Development and Application of Linear Variable Differential Transformer (LVDT) Sensors for the Structural Health Monitoring of an Urban Railway Bridge in Vietnam

Measuring the structure's displacement plays a very important role in ensuring the safe operation of railway bridges in general and urban railway bridges in particular. In Vietnam, traditional methods using high-precision mechanical gauges have been used to measure the displacement of railway bridges. However, these methods need a lot of effort in installation and traffic control during implementation. These methods are based on the static principle: The test loads are placed on the bridge structure, and then the structure's displacement is observed. The safety assessment and analysis results are guaranteed by multiplying the dynamic coefficients, leading to some assessments that may not be close to the actual exploitation of the bridge structure. Therefore, the current study presents a new solution for measuring the displacement of railway bridge structures. This method uses Linear Variable Differential Transformer (LVDT) sensors to record the continuous displacement of the structure during the time the train passes over the bridge. Through field measurements combined with a finite element analysis model, the research focuses on developing and applying LVDT sensors in urban railway bridge structure health monitoring. At the same time, the potential of developing this method in Vietnam in the future is evaluated.

The influence of vehicle dynamics on the time-dependent resonances of a bridge

In bridge structural health monitoring, the response of the bridge while the vehicle is on the bridge, is called a vehicle-bridge interaction (VBI) response. If the vehicle and the bridge are dynamically coupled, the VBI response depends on the bridge’s and the vehicle’s dynamic properties. Therefore, the damage detection techniques based on the bridge resonances become questionable due to the dynamic coupling between the bridge and the vehicle. This study investigates the influence of vehicle dynamics on the bridge’s time-dependent resonances. Vehicle-Induced Delta Frequency (VIDF) represents the changes in the bridge’s time-varying resonances resulting from the vehicle-bridge interaction, while Damage-Induced Delta Frequency (DIDF) accounts for the additional alterations caused by bridge damage. The dynamic interaction between vehicles and bridges (VBIs) is characterized by the frequency ratio between the vehicle (super-system) and the bridge (sub-system). The vehicle frequency is influenced by its dynamics, particularly the suspension systems. Two vehicle models, single suspension and dual suspension vehicles representing passenger trains and freight trains, respectively, are analyzed to assess the significance of vehicle dynamics on VIDF and DIDF. The results demonstrate that both vehicle models experience resonance, which magnifies the dynamic response to damage. However, not all types of vehicles possess the desired dynamic characteristics for effective bridge health monitoring. Trains with single suspension systems exhibit more pronounced changes in the bridge’s frequency response. This characteristic makes them more suitable for effective bridge health monitoring and damage detection.