Bridge Damage Detection Research Articles

Damage detection of plate-type bridges using uniform translational response generated by single-axle moving vehicle

For the first time, the single-axle-two-wheel test vehicle is used to detect the damage of a plate-type bridge that is simply supported at the two longitudinal ends, but free on the lateral sides. The test vehicle with two wheels is refined as a two degree-of-freedom (DOF) system to capture the motions in both the longitudinal and lateral directions. The vehicle response recorded or calculated has been criticized for the vehicle frequencies’ overshadowing effect on the bridge frequencies to be identified in spectral analysis. To overcome this problem, the vehicle-bridge contact response (that is free of vehicle’s frequencies) is used instead to single out the bridge frequencies, while extracting the mode shapes. Central to this study is the newly presented uniform translational response (UTR) for damage localization of the plate, which is physically more meaningful than its predecessor. The UTR vector can be computed for each wheel by considering the first few flexural modes scanned from the contact response along its moving path. Based on the correlation between the two UTR curvatures generated for the left and right wheels of the test vehicle, the location of each damage can be identified. Illustrative examples show that the proposed method works well in estimating the longitudinal locations and lateral extents of the damages in the plate structure. Parametric study has further verified the effectiveness and robustness of the proposed method under various conditions.

Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning

Digital twin (DT) technology has recently gathered pace in the engineering communities as it allows for the convergence of the real structure and its digital counterpart throughout their entire life-cycle. With the rapid development of supporting technologies, including machine learning (ML), 5G/6G, cloud computing, and Internet of Things, DT has been moving progressively from concept to practice. In this article, a DT framework based on cloud computing and deep learning (DL) for structural health monitoring is proposed to efficiently perform real-time monitoring and proactive maintenance. The framework consists of structural components, device measurements, and digital models formed by combining different submodels, including mathematical, finite element, and ML ones. The data interaction among physical structure, digital model, and human interventions are enhanced by using cloud computing infrastructure and a user-friendly web application. The feasibility of the proposed framework is demonstrated via case studies of damage detection of model bridge and real bridge structures using DL algorithms, with high accuracy of 92%.

Bridge damage detection using reconstructed mode shape by improved vehicle scanning method

In this study, the improved vehicle scanning method (VSM) is adopted for structural health monitoring to identify healthy and damaged states of bridges. The improved VSM uses a designed elliptic filter (instead of the conventional Butterworth filter) to obtain the narrowband signals from the contact point response. These high-quality narrowband signals are able to furnish accurate vibration modes that reveal kinks that indicate the damage locations in the bridges. This improved VSM works best with a series of vehicles that are modeled as moving sprung masses passing over the bridge. The exciting vehicles modeled by heavier masses excite the bridge, and they are followed by a testing vehicle modeled by lighter masses to capture the acceleration response at the contact point. Some example problems involving bridges with different damage locations and severities were studied. It is shown herein that the improved VSM extracted mode shapes have very visible kinks to mark the damage positions of the bridges when compared to mode shapes extracted from existing VSMs. So, the improved VSM is able to directly pinpoint the damage locations from the extracted mode shapes with minimal postprocessing effort, whereas existing VSMs generally require additional effort, e.g., constructed damage indices, for damage identification. To better identify the damage locations, a few higher modes should be used so as to avoid missing the damage locations at nodes of a particular vibration mode.

A hybrid structural health monitoring approach for damage detection in steel bridges under simulated environmental conditions using numerical and experimental data

This paper presents a novel hybrid structural health monitoring (SHM) framework for damage detection in bridges using numerical and experimental data. The framework is based on the hybrid SHM approach and combines the use of a calibrated numerical finite element (FE) model to generate data from different structural state conditions under varying environmental conditions with a machine learning algorithm in a supervised learning approach. An extensive experimental benchmark study is performed to obtain data from a local and global sensor setup on a real bridge under different structural state conditions, where structural damage is imposed based on a comprehensive investigation of common types of steel bridge damage reported in the literature. The experimental data are subsequently tested on the machine learning model. It is demonstrated that relevant structural damage can be established based on the hybrid SHM framework by separately evaluating different cases considering natural frequencies, mode shapes, and mode shape derivatives. Consequently, the work presented in this study represents a significant contribution toward establishing SHM systems that can be applied to existing steel bridges. The proposed framework is applicable to any bridge structure in which relevant structural damage can be simulated and experimental data obtained.

Damage Detection of Continuous Beam Bridge Based on Maximum Successful Approximation Approach of Wavelet Coefficients of Vehicle Response

In view of problems such as closed traffic, the large number of sensors required, and the labor-intensive and time-consuming nature of previous bridge detection, this paper analyzes the dynamic response of the vehicle body of the continuous girder bridge under the action of vehicle load. Based on theoretical analysis and formula derivation, a new method of bridge damage detection based on coupled vehicle–bridge vibration is conceived. This method can accurately identify the location of bridge damage and approximately estimate the degree of bridge damage. The method is as follows: Taking the continuous beam bridge as an example, first, use the tractor inspection vehicle model to drive over the continuous beam bridge before and after the damage, and collect the acceleration response of the vehicle body. Then, the acceleration response difference is transformed by wavelet transform. Furthermore, perform the innovative use of the maximum successive approximation approach to process wavelet transform coefficients, which can identify the location of the bridge damage. Additionally, study the impact of vehicle speed, vehicle weight, road surface roughness, and noise on this damage detection method. In addition, a method for judging bridge damage degree based on wavelet transform coefficients is proposed, and the judgment error basically meets the requirements.

A Novel Elastomer-Based Inclinometer for Ultrasensitive Bridge Rotation Measurement.

Bridge deformation consists of cross-section rotation and deflection, which are crucial parameters for bridge capacity evaluation and damage detection. The maximum value of deflection usually happens at mid-span while for rotation it happens at two-ends. Therefore, compared with deflection, rotation is more convenient for in-situ measurement since the bridge pier can be the reference point. In this study, a high-precision inclinometer for bridge rotation measurement was conceptualized, designed, and validated. The proposed inclinometer converted the small rotation of bridge section into the deformation of an elastomer. Strain gauges were then utilized to measure the elastomer deformation and thus the bridge rotation can be obtained. The dimensions and modulus of the elastomer were designed and chosen based on the theoretical analysis. Characteristics of the inclinometer were calibrated in lab and in-situ experiments at an in-service bridge were conducted to validate its feasibility and robustness. Test results showed that the proposed inclinometer had excellent performance in resolution and accuracy, which indicate its great potential for future bridge health monitoring.

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Bridge damage detection via improved completed ensemble empirical mode decomposition with adaptive noise and machine learning algorithms

Bridge Damage and Repair Detection Using an Instrumented Train

The static and dynamic effects of a railway bridge subjected to train loads have been investigated and analyzed to assess its condition. This paper investigates the potential of using repeated dynamic measurements taken on a passing train to determine the condition of a bridge. The results indicated that instrumented trains could be successfully used for ongoing monitoring of condition and, by implication, for identification of the need for repair or rehabilitation. This full-scale approach expands the potential for applications of bridge–vehicle dynamic interaction responses, along with their ability to be demonstrators of successful implementations of decisions on public infrastructure. The work also provided insights into the impact of a stiffer foundation at one pier and greater stiffness in two spans of a viaduct, for which experimental evidence is rarely available. The work also identified some of the challenges of such detection, such as accurate positioning and variability in the speeds of the passing trains.

Damage-Detection Approach for Bridges with Multi-Vehicle Loads Using Convolutional Autoencoder.

Deep learning has been widely employed in recent studies on bridge-damage detection to improve the performance of damage-detection methods. Unsupervised deep learning can be effectively utilized to increase the applicability of damage-detection approaches. Hence, the authors propose a convolutional-autoencoder (CAE)-based damage-detection approach, which is an unsupervised deep-learning network. However, the CAE-based damage-detection approach demonstrates only satisfactory accuracy for prestressed concrete bridges with a single-vehicle load. Therefore, this study was performed to verify whether the CAE-based damage-detection approach can be applied to bridges with multi-vehicle loads, which is a typical scenario. In this study, rigid-frame and reinforced-concrete-slab bridges were modeled and simulated to obtain the behavior data of bridges. A CAE-based damage-detection approach was tested on both bridges. For both bridges, the results demonstrated satisfactory damage-detection accuracy of over 90% and a false-negative rate of less than 1%. These results prove that the CAE-based approach can be successfully applied to various types of bridges with multi-vehicle loads.

Using Two Sensors for Damage Detection of Bridge Based on Transfer Entropy of Vehicle-bridge Information

An innovative damage identification method using only two sensors for damage detection of bridge based on transfer entropy of vehicle- bridge information is proposed in this article. In this method, a test vehicle passing through the bridge surface, and structural vibration acceleration responses at two measuring points are measured from two acceleration sensors, which one set on the bridge, and the other one set on the test vehicle. A fixed moving window is defined as a definitive length calculated by the sampling frequency and the fundamental frequency of the measured acceleration responses. The windowed pair time series extracted from the two measured acceleration responses that obtained from the bridge and the moving vehicle respectively are used to calculate the transfer entropy, which is used to define the new damage index. A simply supported beam bridge subjected to a test vehicle is simulated to verify the effectiveness and feasibility of this method. Numerical simulation results show that this method can accurately detect the damages under both single damage scenarios and multiple damage scenarios using acceleration responses obtained from bridge and moving vehicle. It indicates that the method proposed in this article has a promising application prospect in the field of bridge damage detection.

A data-driven approach for drive-by damage detection in bridges considering the influence of temperature change

This paper proposes the use of a new data-driven approach for drive-by monitoring of bridge condition. The proposed algorithm uses an Artificial Neural Network (ANN) which is trained to predict bridge behavior using acceleration measurements from multiple passes of a traversing vehicle. A simple formulation is presented which allows the response at the point of contact between the tire and the pavement to be inferred from the measurements in the vehicle. The frequency content of this contact-point (CP) response is then used as the primary input to the ANN, along with the vehicle speed. The ANN is also trained to recognize the influence of temperature on the response and a new damage indicator is proposed, allowing the progression of damage over time to be visualized. Results show that using the CP-response in the ANN provides improved performance over the traditionally used axle-response. It is demonstrated that the proposed algorithm is capable of detecting mid-span and quarter-span cracking at all vehicle speeds considered and also in the presence of a rough pavement and varying temperature conditions. The proposed algorithm demonstrates clear benefits over existing drive-by bridge inspection techniques and provides a suitable approach for long term bridge condition monitoring.

Damage detection in bridges under moving loads based on subspace projection residuals

This paper proposes a data-driven method using subspace projection residual of the responses to identify the damage locations in bridges subjected to moving loads. In this method, a moving window with a certain length determined by the sampling frequency and the fundamental frequency of the measured responses is used to cut out the acceleration responses of the bridge subjected to a moving vehicle. The characteristic subspaces of the windowed signals are subsequently extracted to calculate the local damage index using the subspace projection residual. When the window moves to the damage location, the orthogonality between the active subspace of the damaged state and the null subspace of the healthy state is invalid, which leads to a relatively large projection residual that can be used to localize the damage. To improve the reliability of the proposed approach, a one-side upper confidence limit is introduced. A simply supported beam bridge subjected to a moving mass is simulated to verify the effectiveness of the proposed method. Numerical results indicate that the proposed approach can accurately localize the single and multiple damages, even when the responses are smeared with a significant noise. Experimental tests conducted on a steel beam bridge model also demonstrate the performance and accuracy of the proposed approach. The results demonstrate that the proposed method can localize the damage even with a small number of sensors, indicating the method has a good and promising performance for practical engineering applications.

Feasibility investigations on bridge damage detection using BWIM focusing on changes in deflection influence line

Feasibility investigations on bridge damage detection using BWIM focusing on changes in deflection influence line

A Novel Derivation of Stationary Vehicle Dynamic Responses and the Use of Stationary Vehicle for Bridge Damage Detection

A Novel Derivation of Stationary Vehicle Dynamic Responses and the Use of Stationary Vehicle for Bridge Damage Detection

Structural Crack Identification Based on the Variational Mode Decomposition

In order to enrich the method of bridge damage detection and further improve the accuracy of bridge damage identification, a detection method of simply supported beam with crack under dynamic load is proposed, which is not based on the complete finite element model. Under the premise of not blocking traffic, the method only needs to analyze and deal with the acceleration response of simply supported beam span, which reduces the loading, unloading and maintenance of sensors in practical engineering. At the same time, based on the model, an analytical formula of acceleration in the mid-span of simply supported cracked beam is derived. Supported by theoretical derivation, instantaneous energy and mean energy difference are constructed by using variational mode decomposition and Hilbert transformation, which can effectively identify small cracks with 5% crack depth. Based on this, the influences of different wheel load, environmental noise and damage degree on detection results are studied. The results show that: ① The instantaneous frequency has a better recognition effect on crack location; ② The mean energy difference is sensitive to different crack depth ratio δ and wheel load; ③ This method has strong noise robustness.

Damage detection of bridges under changing environmental temperature using the characteristics of the narrow dimension (CND) of damage features

Latent environmental variable-based methods are popular for diagnosing structural damage in bridges under varying environmental temperatures. However, it is still a shortage of researches on distinguishing and selecting the damage features suitable for this type of method in order to improve the validity of damage detection in terms of their application to actual bridges. To address this issue, a method based on the characteristics of the narrow dimension (CND) of damage features is proposed for damage detection in actual bridges under changing environmental temperatures. First, the definition of the CND of damage features is presented in detail by analysing the influencing factors of latent environmental variable-based methods for damage detection. Second, based on the correlations among damage features, an index is presented for determining whether the damage features possess CND. If so, these damage features are suitable for latent environmental variable-based methods, which are beneficial for detecting damage to bridges while considering environmental influences; if not, then other types of methods must be considered in using the above damage features. Consequently, combined with novelty detection and variational mode decomposition, a method based on CND is implemented for detecting the damage to actual bridges. Finally, examples of numerical simulations and actual bridges are provided to demonstrate the effectiveness of the proposed method.

Migration Learning-Based Bridge Structure Damage Detection Algorithm

Most of the bridge structures in the world are built of reinforced concrete. With the growth of service life and the increase of urban traffic and other factors, most bridges put into service have more or less damage. Traditional bridge damage detection methods include the manual inspection method and bridge inspection vehicle method, which have many shortcomings. Moreover, the detection of cracks in bridges is critical to the safety of transportation due to the extremely large number of bridges built in the road networks across the world. To this end, this paper uses the most widely used CNN in deep learning to identify and classify crack images and proposes a migration learning technique to solve the problem of the large amount of training data required for training CNN. The data augmentation and sliding window techniques are introduced to divide the collected crack data into training establish and test set. The experiments show that the method in this paper can classify the crack images better, extract and locate the cracks of bridge crack units, and finally extract the crack coordinates of boxing. Compared with the customary image recognition methods, the method used in this paper is easier to operate in practical engineering, and the accuracy of the obtained results is higher.

Damage detection of warren truss bridge using frequency change correlation

The detection of structural damage in any structure becomes the primary concern in failure analysis. Early detection of structural failure is crucial for preventing hazards and losses caused by structural failure. There are various approaches for detecting structural damage, and these approaches must be developed in large numbers for structural safety.The purpose of this study is to provide a simple and effective method for estimating the damage to a warren truss bridge utilizing frequency change correlation and the results of the modal analysis performed using SAP 2000 FEA software. The numerical results of the first five-mode shapes and frequencies of the Louisville bridge model were validated for modal analysis accuracy in SAP 2000. The primary variable in the modal analysis was the presence of six different sections of hollow mild steel pipe with an outer diameter of 10, 12, 14, 16, 18, and 20 mm and a 2 mm plate thickness. Three different locations for damage have been considered in the warren truss bridge. The first six modes' natural frequencies and modal participation factors were compared for undamaged and damaged warren truss bridges. The results indicate that as the diameter of warren truss bridge sections increases, the natural frequency and modal participation factors of the first six modes remain constant in the undamaged case but increase in the three-damage scenario. Additionally, there was a slight change in natural frequency and modal participation factor for three damage scenarios. As a result of increasing the natural frequency and modal participation factor for damage scenarios, the health of the warren truss bridge may be monitored, and appropriate measures adopted before it fails.

Vertical vehicle displacement based drive-by inspection of bridge damage with parameter optimization

Recently, there has been an increasing emphasis on the Indirect bridge health monitoring method employing passing vehicles, which is regarded as one of the most effective approaches in bridge damage screening. However,few researches have been conducted on the drive-by bridge inspection method using vehicle displacement profile as damage indicator. This paper proposes a new drive-by inspection method based on vertical vehicle displacementprofile with parameter optimization. A generalized Vehicle-Bridge Interaction (VBI) system is built in MATLAB, where the bridge is modelled as a simply supported beam with 10 elements, and the passing vehicle is represented as a simplified quarter car. To improve the result sensitivity to bridge damage, the parameter optimization of vehicle configuration is processed employing the Monte Carlo methods. Results show that the proposed method can successfully detect and localize bridge damage by using vertical vehicle displacement profile as damage indicator only, and its performance may depend on the vehicle configuration. The proposed approach provides merits in simplicity and efficiency, which can be applied widely to the bridge damage detection problems.

Bridge Damage Detection Through Combined Quasi-static Influence Lines and Weigh-in-motion Devices

Bridge Damage Detection Through Combined Quasi-static Influence Lines and Weigh-in-motion Devices