Bridge Health Monitoring Research Articles

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.

Review and Prospect of Research on Structural Health Monitoring Technology for Bridges

As a crucial infrastructure in the transport system, the safe operation of bridges is directly related to all aspects of people’s daily lives. The development of bridge structural health monitoring technology and its application play an important role in ensuring the safety and extending the service life of bridges. This paper carries out in-depth research and analysis on the related technology of bridge structural health monitoring. Firstly, the existing monitoring technologies at home and abroad are sorted out, and the advantages and problems of various methods are compared and analyzed, including nondestructive testing, stress measurement, vibration characteristic identification, and other commonly used monitoring technologies. Secondly, the key technologies and equipment in the bridge health monitoring system, such as sensor technology, data acquisition, and processing technology, are introduced in detail. Finally, the development trend in the field of bridge health monitoring is prospected from both theoretical research and technical application. In the future, with the development of emerging technologies such as big data, cloud computing, and the Internet of Things, it is expected that bridge health monitoring with intelligent and systematic features will be more widely applied to provide a stronger guarantee for the safe and efficient operation of bridges.

Coupled Vibration of a Vehicle Group–Bridge System and Its Application in the Optimal Strategy for Bridge Health Monitoring

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

Improved Blob-Based Feature Detection and Refined Matching Algorithms for Seismic Structural Health Monitoring of Bridges Using a Vision-Based Sensor System

Improved Blob-Based Feature Detection and Refined Matching Algorithms for Seismic Structural Health Monitoring of Bridges Using a Vision-Based Sensor System

Structural Health Monitoring of Bridges with Personal Laser Scanning: Segment-based Analysis of systematic Point Cloud Deformations

Abstract. Bridge structures can be surveyed using a number of different methods. Established are image-based methods using structure from motion by an unmanned aerial vehicle (UAV), terrestrial laser scanning (TLS), or a combination of both methods. Beyond static terrestrial laser scanning, buildings can also be efficiently surveyed using personal laser scanner (PLS) systems. The advantage here is the greater flexibility and increased speed compared to the static method. On the other hand, the accuracy may be more critical, and the resulting point cloud will be more sensitive to systematic global or local deformations under unfavorable measurement conditions. For example, temporary influences can lead to local mapping errors. These include influences such as uneven measurement system motion or non-static, feature-sparse environments. This study investigates the acquisition of 3D point clouds representing the outer shell of a concrete bridge using a PLS system. We demonstrate a method for detecting possible deformations in PLS point clouds using the example of a bridge structure. For this purpose, the reference (TLS) and the PLS point clouds are segmented into individual clusters and a segment-based ICP fine registration is performed. Different RMSE values for the upper road section (0.061 m) and for the pillar segments (0.021 m) as well as different transformation parameters indicate slight displacements in the PLS point cloud. The analysis of the cloud-to-cloud distances showed that there were slight deformations in the Z direction in the area of the road surface. In the lateral direction, no significant residual deviations were found in the area of the bridge pillars.

Bladeless Wind Turbine Triboelectric Nanogenerator for Effectively Harvesting Random Gust Energy

AbstractIn the environment, there is an abundance of gust energy which is challenging to harvest with conventional rotating wind turbines, such as the gusts generated by passing vehicles along roadsides. Addressing the irregular and low‐frequency characteristics of gusts, a bladeless wind turbine triboelectric nanogenerator (BWT‐TENG) with enhanced aerodynamic performance is proposed, enabling effective harvesting of random gust energy. First, a bladeless wind turbine with a cylindrical bluff body shape is designed, and its aerodynamic principles under gust‐driven conditions are elucidated through the computational fluid dynamics method. Subsequently, parameter optimization is conducted for the multilayered TENG. Systematic experiments demonstrated that the BWT‐TENG achieved a peak power density of 4.08 W m−3 driven by a gust of 10 m s−1, and can even operate at frequencies as low as 0.1 Hz. Finally, experiments showcased the BWT‐TENG powering a warning light in a simulated rainfall environment and harvesting gust energy from vehicles passing by real roadside to power wireless gyroscopic sensors, thereby achieving self‐powered structural health monitoring of roads or bridges. This work provides a novel strategy for utilizing TENGs in the harvest of environmental gust energy and demonstrates the vast potential of TENGs in the field of self‐powered structural health monitoring.

A data recovery method for extra-long-span railway bridge health monitoring based on TVFEMD and CNN-GRU

Structure health monitoring systems play a crucial role in understanding the condition of structures. However, owing to various uncertain factors, sensor data may be missing, which poses a great challenge to capture the real-time dynamic characteristics of the bridge. To address this problem, this paper proposes a model that integrates time varying filtering based empirical mode decomposition (TVFEMD), convolutional neural network (CNN), and gated recurrent unit (GRU) to forecast and fill in the missing data. The proposed model initially employs TVFEMD to decompose the signal into several intrinsic mode functions (IMFs) of different frequency bands. Subsequently, CNN is utilized to extract data features for each IMF, followed by prediction through GRU. The model linearly combines the prediction results obtained from each IMF to obtain the actual prediction result. Simulation data and measured data from the large railway bridge are utilized in this research to validate the model’s efficacy. The analysis results demonstrate a significant improvement in prediction performance compared to traditional models, showcasing strong generalization ability and robustness. In conclusion, the model proposed in this paper has a high utilization value in health monitoring data recovery.

Identification of bridge influence line and multiple-vehicle loads based on physics-informed neural networks

Influence lines (ILs) and vehicle loads identification are critical in the design, health monitoring, and damage detection of bridges. Traditionally, the approach used in most existing literature has been to solve the system of equations directly. However, these approaches require complex calculations such as matrix decomposition and regularization coefficient optimization, making them difficult to implement. In addition, there are difficulties in obtaining accurate axle information and effectively separating the bridge response due to each vehicle. Thus, the improvement of identification algorithms for ILs and multi-vehicle loads remains of significant importance. To address these issues, this paper presents a novel approach that integrates prior physical equations and neural networks. This is achieved by integrating the equation that reflects the relationship between axle loads and bridge response into the neural network, utilizing existing methods for acquiring axle information of vehicles. To validate the effectiveness of the proposed method, it was first applied to theoretical and simulation data. The study then investigated the impact of noise and dynamic effects on the accuracy of the results, as well as the range of the neural network layers and sampling intervals. Finally, the method was implemented for identifying multiple-vehicle loads. The findings of the study confirm the feasibility and numerical stability of the proposed approach. The proposed method eliminates the need for complex computational processes, including matrix decomposition, diagonalization, regularization coefficient optimization, and solution vector smoothing fitting. As a result, the implementation of the algorithm is significantly less challenging, and identification accuracy is improved. It is important to note, however, that the proposed method is relatively more time-consuming due to the iterative learning and training required by the neural network.

Using machine learning algorithms for predicting bridge vertical deflection

Machine learning (ML) is being increasingly used to ease structural health monitoring and management of bridges. Therefore, the main objective of this study is to apply two state-of-the-art ML algorithms, namely locally weighted learning (LWL) and K-Star for the prediction of vertical deflection of composite bridges. To accomplish the objective, 83 track loading tests were carried out at various bridges located in Vietnam and deflection data was collected for models’ development. Model's validation and comparison were carried out using different popular methods, namely mean absolute error, root mean squared error and R on both training (70%) and validation (30%) datasets. The results of this study indicate that the K-Star algorithm outperforms LWL in predicting the vertical deflection of composite bridges. Consequently, K-Star can serve as an effective tool for rapidly predicting vertical deflection, thereby facilitating more efficient bridge health monitoring and management. This efficiency can lead to significant time and cost savings in maintaining the structural integrity of composite bridges.

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

Acoustic emission technology-based waveguide localization method for damaged internal tendons of in-service post-tensioned hollow slab bridges

Prestressing tendons are crucial load-bearing elements in post-tensioned hollow slab bridges. Damage to these tendons significantly reduces the safety, functionality, and longevity of the bridge. Regular monitoring of prestressing tendons is essential for bridge operation. Acoustic emission technology, known for its high accuracy and sensitivity to minor vibrations, is a key technique for monitoring bridge cables. This study proposes a novel dual-sensor acoustic emission localization method for detecting tendon damage in post-tensioned hollow slab bridges, utilizing waveguide technology. The effectiveness of this method is confirmed through hammering tests that simulate acoustic emission damage on operational hollow slab bridges. The proposed method, requiring only two sensors, offers a cost-effective alternative for monitoring the entire span of post-tensioned hollow slab bridges, so that effectively reduces the cost of bridge health monitoring and is easy to be promoted in actual engineering projects.

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.

Evaluation of bridge structure damage based on deep learning identification model

Safety monitoring is an important part of bridge engineering construction and operation. At present, there is room for promoting the health monitoring and evaluation of small and medium-sized concrete bridges. In view of this, the study first models the spatial model and physical parameters of the bridge, and then builds the data of vehicle load and vehicle type. To reduce the complexity of data mapping, wavelet packet decomposition is used to analyze the data structure. And the physical field effect analysis is abandoned to directly mine the data relationship at both ends by using deep neural network. The data decomposition results show that the method can discard the temperature-induced effect. And the local decomposition results of the data meet the input of the neural network. The data measured by the sensor is added to the depth learning model for fitting. The overall and local fitting rates are more than 92%. The loss function converges quickly, and there is no gradient explosion. The model predicts the bridge structural damage caused by vehicle stress of four load categories, and the results show that the average fitting rate is 89.72%. Therefore, the identification path of the proposed deep learning model has positive significance for the evaluation of bridge structural damage. The main contribution of the study is to propose a deep learning-based method for bridge structural damage assessment. By modeling the spatial model and physical parameters of the bridge and combining data from vehicle load and vehicle type, the data structure was analyzed using the wavelet packet decomposition method to eliminate temperature-induced effects and data from sensor measurements were added to the deep learning model for fitting. This finding has positive implications for bridge structural damage assessment and can provide effective pathways and methods to monitor and evaluate the health status of small and medium-sized concrete bridges. This has important practical application value for the construction and operation of bridge engineering.

On population-based structural health monitoring for bridges: Comparing similarity metrics and dynamic responses between sets of bridges

Bridges are valuable infrastructure assets that are challenging and expensive to maintain. State-of-the-art data-based bridge SHM solutions look to use bridge response data for condition assessment and damage detection. Data-based SHM methods can be limited in their application as they require large datasets to train models effectively, and most bridges lack the available data for the approaches to work. Further, it would be expensive and unrealistic to collect the required datasets to employ data-based methods to entire bridge networks. Recently, a population-based structural health monitoring (PBSHM) approach was proposed that seeks to leverage the data available for SHM problems by pooling together similar structures with their datasets. The PBSHM approach could be valuable in bridge SHM, enhancing the datasets available for ‘populations of bridges. The PBSHM approach for assessing the similarity of bridges has been considered before and was shown to be useful for identifying similar and different bridge types. However, no data were considered in the previous work, and similarity metrics were only qualified using engineering judgement. For the PBSHM approach to be useful in bridge SHM, there is still a need to check that ‘similar’ bridges have similar responses for transfer learning to be feasible. This paper expands upon previous work and provides originality by investigating if bridges identified as similar also exhibit similar responses. The PBSHM derived similarity metrics convey the topological similarity between structures, and mode shapes are identified as being a topologically sensitive bridge response. Therefore, a modal test campaign is carried out for a set of six real bridges, and Operational Modal Analysis is used to identify modal responses from each of the bridge decks. The Modal Assurance Criterion is used to evaluate the similarity between the mode shapes from pairs of bridges and is subsequently compared to the similarity metrics evaluated between those bridges. The similarity metrics were found to be reflective of the similarities identified between the respective bridges’ mode shapes for bridges of the same and different types. The significance of this finding is that it is an important step towards validating the PBSHM comparison approach for identify similar structures where transfer learning might be attempted.

Transportation bridge infrastructure asset condition monitoring using uncrewed aerial vehicles (UAVs)

The optimal performance of a road transportation network depends on the serviceability and reliability of bridges connecting the pavements. Bridge performance depends on several geotechnical assets among which abutments play a key role as they support the bridge span, and transfer the vertical and horizontal loads from the superstructure to the foundation. Soil abutments are designed to be stable against overturning and sliding and prevent differential settlement and excessive lateral movements. In the United States, 42 % of bridges are observed to be over 50 years old. Department of Transportation (DOT) agencies periodically monitor and assess the bridge conditions typically every two years. Due to the costs and the time involved in conducting inspections using traditional data collection tools, DOTs are exploring innovative technologies to conduct inspections and realize benefits in terms of access, cost, and safety. They have widely adopted the use of uncrewed aerial vehicles (UAVs) for structural health monitoring of bridges. A few studies also conducted 360⁰ bridge inspections using camera sensors mounted on UAVs in the recent past. In this current study, the US Federal Aviation Administration (FAA) guidelines for commercial use of drones were discussed and followed in field studies. Further, UAVs were demonstrated for conducting 360° bridge inspections and identifying the erosion near abutments using three-dimensional models. This paper presents the application of innovative technologies for geotechnical assessments of two bridges in the Alaska region, with a focus on assessing the need for rehabilitation measures. These new technologies have become essential for reliable condition monitoring post-disaster events as well. This approach is expected to benefit the agencies in managing the transportation geotechnical assets in a timely and cost-effective manner.

1D in-situ convolution system based on vibration signal for real-time structural health monitoring

Structural health monitoring (SHM) of bridges and other building structures has been widely concerned by researchers and engineers. SHM achieved a breakthrough by using convolutional neural networks (CNNs) in deep learning. However, the traditional vision based SHM can not monitor the invisible structural damage. The traditional vibration based SHM needs to convert 1D signals into 2D images, which is easy to lose information in the conversion process, and requires a large scale of hardware circuit. In this paper, a 1D in-situ convolution system (ICS) based on vibration signal is proposed. The SHM is realized by 1D convolution calculation of the structural vibration signal detected by a high-sensitivity triboelectric nanogenerator (TENG) through one in-situ convolution transistor. Based on the natural adaptability of TENG to 1D convolution calculation, and combined with the ability of a single in-situ convolution device to replace more than eight multi conductance devices, ICS can reduce the required SHM computing array area by over 87.5%. Finally, the real-time identification of different positions and different damage levels of the steel bridge was realized by simulating the vibration response of the steel bridge.