Bridge Damage Research Articles

GRNN-based cascade ensemble model for non-destructive damage state identification: small data approach

AbstractAssessing the structural integrity of ageing structures that are affected by climate-induced stressors, challenges traditional engineering methods. The reason is that structural degradation often initiates and advances without any notable warning until visible severe damage or catastrophic failures occur. An example of this, is the conventional inspection methods for prestressed concrete bridges which fail to interpret large permanent deflections because the causes—typically tendon loss—are barely visible or measurable. In many occasions, traditional inspections fail to discern these latent defects and damage, leading to the need for expensive continuous structural health monitoring towards informed assessments to enable appropriate structural interventions. This is a capability gap that has led to fatalities and extensive losses because the operators have very little time to react. This study addresses this gap by proposing a novel machine learning approach to inform a rapid non-destructive assessment of bridge damage states based on measurable structural deflections. First, a comprehensive training dataset is assembled by simulating various plausible bridge damage scenarios associated with different degrees and patterns of tendon losses, the integrity of which is vital for the health of bridge decks. Second, a novel General Regression Neural Network (GRNN)-based cascade ensemble model, tailored for predicting three interdependent output attributes using limited datasets, is developed. The proposed cascade model is optimised by utilising the differential evolution method. Modelling and validation were conducted for a real long-span bridge. The results confirm the efficacy of the proposed model in accurately identifying bridge damage states when compared to existing methods. The model developed demonstrates exceptional prediction accuracy and reliability, underscoring its practical value in non-destructive bridge damage assessment, which can facilitate effective restoration planning.

A portable real-time concrete bridge damage detection system

A drone detection system can achieve real-time inspection of concrete bridge damage. However, it is difficult to deploy existing detection algorithms on a portable system due to their high computational cost. Therefore, a portable damage detection algorithm called CD-YOLOv8 is designed. CD-YOLOv8 is operated on Jetson Xavier NX to detect drone-captured bridge images in real-time. The multi-scale feature adaptive fusion module is used to reduce model parameters and improve detection speed. The spatial context pyramid and deformable convolutional structure are introduced to improve the detection accuracy of tiny concrete bridge damage. Compared with the YOLOv8n, the mAP50 of CD-YOLOv8 is increased by 3.6 %, while the model parameters and FLOPs are decreased by 0.05 M and 0.7 G, respectively. CD-YOLOv8 is also tested on the Jetson Xavier NX with a detection speed of 44 FPS, which is 15.79 % faster than the YOLOv8n. Therefore, CD-YOLOv8 has high accuracy and excellent real-time performance.

Post-earthquake damage probability assessment of high-speed railway simply supported bridge based on traffic capacity

Railway transportation is an important channel for emergency rescue and disaster relief. Traditionally, the method of evaluating the damage degree of bridges under different seismic intensities from the perspective of structural damage cannot be directly used to judge the traffic capacity of trains on bridges after earthquakes. It is impossible to guarantee the running safety and affect the transportation of materials after the earthquake. Because the damage state of track structure directly affects the running safety of trains, this study combines the relationship between the damage of track structure and the deformation of bridge structure after earthquake, and puts forward the transverse relative displacement of girder end (TRDGE) as the damage index of train running function of railway bridge after earthquake. Then, the OpenSees-TRBF (Train-Railway-Bridge-Foundation Coupled System Dynamic Analysis Platform) co-simulation method is used to carry out the train-track-bridge coupling dynamic calculation under different seismic damage states, and the running safety analysis database under different TRDGEs is established. Then, the Particle Swarm Optimization (PSO) algorithm is used to classify the bridge damage index of traffic capacity through different target safety speeds. Based on the probabilistic seismic demand model, the exceedance probability curve of traffic capacity damage under different seismic intensities is established. Finally, based on the curve, the running safety risk domain of trains on the bridge under different seismic intensities is obtained with a confidence level of 95 %. This study proposes a post-earthquake damage assessment method for railway bridges based on the assurance of running safety probabilities, evaluating the damage degree of the bridge with the residual traffic capacity of the bridge after the earthquake. This study can be applied to assess earthquake damage on existing railway bridges and provide references for the safe speed of trains on the bridge after an earthquake.

Drive-By Identification of Joint Bridge Damage and Surface Roughness Based on Sensitivity Analysis of Residual Contact Point Deflections

Drive-by inspection of bridge damage using a passing vehicle’s dynamic response has great potential in bridge damage identification. Among the current drive-by methodologies, the methods based on bridge modeling have been proposed for the quantitative identification of bridge damage and bridge surface roughness. However, the coupling of bridge damage and unknown surface roughness increases the difficulty of the identification problem and most previous studies conduct the identification task of bridge damage or bridge surface roughness separately. In this paper, a novel method is proposed for the identification of joint bridge damage and bridge surface roughness based on the residual bridge deflections from the front and rear wheels contact points of an instrumented passing vehicle. The vehicle–bridge interaction is analyzed using a half-car model of a four degrees of freedom (4-DOF) with front and rear vehicle wheels. First, the vehicle–bridge unknown forces and displacement of the vehicle axles are identified by the generalized Kalman filter under unknown input (GKF-UI) proposed by the authors. The sensors can be installed conveniently on the vehicle body due to GKF-UI. Then, the residual bridge deflections from the front and rear vehicle wheels contact points are utilized to eliminate the effect of road surface roughness. Afterward, bridge structural damage is identified based on the sensitivity analysis of residual bridge deflections from the front and rear contact points with [Formula: see text]1-norm regularization. Finally, bridge road surface roughness is estimated based on the identified unknown forces and updated bridge model with damage. The results of numerical identification examples demonstrate the effectiveness of the proposed method for the identification of joint bridge damage and surface roughness.

Effect of Ground Motion on Fragility and Vulnerability of Reinforced Concrete Bridge in Different Soil Conditions

Bridges are essential components of transportation networks, serving as lifelines for the movement of people and goods. The bridges' resilience to natural disasters, particularly earthquakes, is an issue of paramount significance. Yet, a critical knowledge gap exists when it comes to understanding how bridges perform in the challenging terrain of medium-density sandy and clay soils during seismic events. This study seeks to bridge that gap by developing fragility curves that quantitatively evaluate the likelihood of bridge damage or failure across varying levels of earthquake magnitudes. The CSI Bridge v25.0.0 was used to simulate earthquake ground motions specifically within medium-density sandy and stiff clayey soils. Nonlinear time history analyses of the bridge were performed by using 5 different earthquake events with PGA ranging from 0.25 – 1.5g. Fragility analysis is performed to develop seismic fragility curves for the critical part of the bridge for various peak ground acceleration (PGA) in two types of soil conditions. The findings revealed that the deck component is more susceptible to damage than the pier. The impact of seismic activity on medium-dense soil is more significant than on stiff clayey soils in relation to the critical components of the bridge. This result enhances the ability to design bridges that can withstand and recover from seismic events more effectively.

Hybrid Approach for Supervised Machine Learning Algorithms to Identify Damage in Bridges

Hybrid Approach for Supervised Machine Learning Algorithms to Identify Damage in Bridges

Indirect Identification and Analysis of Bridge Damage Using Vehicle–Bridge Coupled Vibration and Deep Learning

Indirect Identification and Analysis of Bridge Damage Using Vehicle–Bridge Coupled Vibration and Deep Learning

Transfer learning for structure damage detection of bridges through dynamic distribution adaptation

In recent years, the rapid increase in the number of bridges not only brings convenience to people but also means that more bridges are at potential safety risks. How to quickly and accurately identify damage that occurs in bridges is a primary problem for society to solve. Machine learning, which can train a classifier for bridge damage detection from existing labeled data, has been widely applied to damage detection of bridge structures. However, the method can have limitations such as the difficulty of collecting sufficient damage data, the heavy workload of the data labeling task, and the failure of the classifier when the bridge structure changes, which limits the application of machine learning in structural damage detection. In order to achieve damage detection of bridges with different spans without target labeling, a dynamic distribution adaptation network (DDAN) containing a feature extraction module and a dynamic distribution adaptation (DDA) module is designed in this paper. DDAN uses the feature extraction module to extract features, and then the DDA module acquires the extracted features to dynamically adapt the probability distribution for calibrating the features in the source and target domains which makes the feature distributions in the source and target domains approximate. In order to verify the feasibility of this paper's method, DDAN is applied to the damage detection of simply supported box girder bridges with different spans through numerical simulation in this paper, where the damage features of a 32 m bridge (source domain) are transferred to a 24 m bridge (target domain), and damage localization and quantification of the target bridge is achieved without damage labels on the target bridge. The results show that DDAN possesses a better classification effect as well as faster convergence speed than the network without domain adaptation and traditional deep domain adaptation networks, and has important application prospects in damage detection of bridges with different spans.

Bridge damage identification based on synchronous statistical moment theory of vehicle–bridge interaction

AbstractConsidering the weak noise resistance and low identification efficiency of traditional bridge damage identification methods, a data‐driven approach based on synchronous statistical moment theory and vehicle–bridge interaction vibration theory is proposed. This method involves two main steps. First, a two‐axle test vehicle is used to collect acceleration response signals synchronously from adjacent designated measurement points while stationary. This operation is repeated to calculate the second‐order statistical moment curvature (SOSMC) difference of entire bridge points corresponding signals in different states. By comparing with the reference value, the preliminary damage location of the bridge can be obtained. Second, the first‐order modal shape curve is constructed using the second‐order statistical moment (SOSM). The refined identification of bridge damage is then based on an improved direct stiffness back calculation of the bridge's stiffness. This article proposes the synchronization theory for the first time and combines it with the statistical moment clustering method, forming an innovative approach to obtaining structural vibration modes. The effectiveness of this method has been well validated through numerical simulations with different parameters and on‐site bridge tests. The research results indicate that SOSMC indicators have better noise resistance and higher recognition efficiency in identifying damage locations, compared to modal curvature and flexibility curvature indicators. Additionally, compared to transfer rate and random subspace methods, the SOSM method results in smaller error and higher identification efficiency.

A novel CNN architecture for robust structural damage identification via strain measurements and its validation via full-scale experiments

In this study, an innovative two-dimensional Convolutional Neural Network (2D CNN) architecture is proposed and investigated for the classification of bridge damage. Employing unique strain time-history data transformed into grayscale images, the approach seamlessly combines feature extraction and classification, allowing for the precise identification and categorization of structural damage. The method’s effectiveness was validated through field experiments on a full-scale bridge mock-up sample subjected to several controlled damage states under nonstationary, commercial vehicle loads. A wide range of realistic damage conditions, from minor to severe structural damage states, was included in the experimental scenarios together with inherent operational uncertainties. The robustness of the 2D CNN model was rigorously tested against fluctuating loads and introduced noise. Demonstrating remarkable accuracy, the 2D CNN successfully classified different damage states with over 95% accuracy, effectively identifying a damage state that was visually undetectable. Furthermore, the architecture proved to be highly versatile, effectively handling variations in the number of sensors. uncertainties included in the experimental data, and elevated levels of measurement noise.

Seismic vulnerability assessment of RC deck-stiffened arch bridges

Due to the recent damage and collapse of several bridges in Italy, reliable quantification of their vulnerability is paramount for planning various risk mitigation and resilience-enhancing strategies. This paper specifically focuses on the seismic vulnerability and loss assessment of existing reinforced concrete (RC) deck-stiffened arch bridges. The study considers a bridge typology known as the “Maillart–type” arch bridge, a common structural typology in the 1950s and characterised by a particularly stiff deck and a slender vault, i.e., a high deck-to-arch flexural stiffness ratio. Such bridges were typically designed considering only gravity loads and usually without ductile structural detailing and consideration of capacity-design principles. Historical documents and original design blueprints are used to simulate the design of an archetype bridge representative of the Italian “Maillart-type” arch bridge population. Then, nonlinear cloud-based time-history analysis is performed to investigate the seismic response of the considered archetype under varying ground-motion intensities and derive fragility and vulnerability relationships. Specifically, three different models are considered: one that adopts a conservative approach where damage is determined by the shear failure of the shortest pillar, another that provides a more detailed representation of structural damage progression, and a third that assumes retrofitting of the shortest pillar. The vulnerability relationships are combined with site-specific seismic hazard curves for case-study locations to estimate expected annual losses. The results represent a first step in assessing the seismic vulnerability of “Maillart–type” arch bridges to plan seismic risk mitigation interventions for such a bridge typology.

Experimental and numerical investigation on seismic performance of novel cable-restraining composite rubber bearings for highway bridges

Introducing cable restrainers into isolation bearings is promising to enhance the seismic performance of highway bridges. However, limitations exist in cost, performance, and analysis models of current products typically composed of flat sliding bearings (FBs) and separated cables. Therefore, this study proposes a novel cable-restraining composite rubber bearing (CRCRB) comprising a composite rubber bearing (CRB) as the bearing body and continuous cables. Cyclic tests were conducted on 12 specimens, including cable-restraining FBs and CRCRBs. Then, restoring force models of the bearing bodies were developed via theoretical analysis. Those of the cables were obtained from numerical models based on derived cable profiles. Finally, incremental dynamic analysis (IDA) was performed to evaluate the effects of the CRCRBs and cable models on structural responses. The test results verify the feasibility of the new cable layout and the advantages of the CRBs as the bearing body in restoring force, damping capacity, and residual displacement. As both the radii of the curved segments and the inclination angle of the linear one in the cable profiles decreased, the cable stiffness kept rising during loading. Cable sliding at clamps enlarged the cable-free displacement and residual deformation, and was relieved by adding clamps. The proposed models fit the test data well. The IDA results illustrate the superiority of the CRCRBs in mitigating bridge damage and highlight the importance of using the precise multi-linear cable model instead of the simplified linear one to avoid overestimating responses.

Detection of bridge damage through analysis of dynamic response to vehicular loads utilizing long-gauge sensors

PurposeThis paper presents a novel method for identifying damage in reinforced concrete (RC) bridges, utilizing macro-strain data from distributed long-gauge sensors installed on the concrete surface.Design/methodology/approachThe method relies on the principle that heavy vehicles induce larger dynamic vibrations, leading to increased strain and crack formation compared to lighter vehicles. By comparing the absolute macro-strain ratio (AMSR) of a reference sensor with a network of distributed sensors, damage locations can be effectively pinpointed from a single data collection session. Finite-element modeling was employed to validate the method's efficacy, demonstrating that the AMSR ratio increases significantly in the presence of cracks. Experimental validation was conducted on a real-world bridge in Japan, confirming the method's reliability under normal traffic conditions.FindingsThis approach offers a practical and efficient means of detecting bridge damage, potentially enhancing the safety and longevity of infrastructure systems.Originality/valueOriginal research paper.

Effect of bridge height on airflow and aeolian sand flux near surface along the Qinghai-Tibet Railway, China

In this work, we studied the near-surface flow field structure of railway bridges with different heights through field investigation and wind tunnel simulation experiments. Meanwhile, we simulated the distribution of sand accumulation around a bridge via CFD software based on the sand accumulation around the Basuoqu bridge in the Cuona Lake section of the Qinghai–Tibet Railway. Results show that the sand around this railway bridge is mainly from the lake sediment on the west side of the railway and the weathered detritus on the east side. The height of the railway bridge in the sandy area affects the distribution of the near-surface flow field and the variation in speed on both sides of the bridge. The wind speed trough on both sides of the 6 m high bridge is higher, and the horizontal distance between the wind speed trough and the bridge section is 1.5 times that of the 3 m high bridge. Wind speed attenuates in a certain range on the windward and leeward sides of the bridge, forming an aeolian area; under the beam body, it is affected by the narrow tube effect, forming a wind erosion area. The height of the bridge determines its sand transport capacity. Under certain wind conditions, the overhead area at the bottom of the 3 m high bridge and its two sides do not have the sand transport capacity, so sand accumulates easily. Nevertheless, the sand accumulation phenomenon gradually disappears with the increase in bridge clearance height. The objectives of this study are to reveal the formation mechanism of sand damage for railway bridges, provide theoretical support for the scientific design of railway bridges in sandy areas, and formulate reasonable railway sand prevention measures to ensure the safety of railway running, which have certain theoretical significance and practical value.

Enhancing bridge damage assessment: Adaptive cell and deep learning approaches in time-series analysis

In recent years, the application of Deep Learning (DL) for damage detection in Structural Health Monitoring (SHM) using time-series data has garnered significant attention from the global scientific community. By incorporating numerous filters and units in their architecture, these models facilitate feature extraction making the processing of large time-series datasets in SHM more effective. However, optimizing of the number of filters and units is a challenge, often relying on scientists' experience or previous models without a systematic method for measurement or automatic selection. Therefore, this study proposes a novel framework to enhance the accuracy and efficiency of SHM for damage detection in bridge structures by using metaheuristic algorithms, Electric Eel Foraging Optimization (EEFO) algorithm, to optimize hyperparameters of DL model. This DL model combines the advantages of each traditional model. Specifically, 1D Convolutional Neural Network (1DCNN) is employed for features extraction, Gated Recurrent Units (GRU) for identifying long-term dependencies, and Residual Networks (ResNet) for avoiding vanishing gradient problem which often happens in DL model during training, referred to as 1DCNN-GRU-ResNet. The time-series dataset generated from Cua Rao bridge is used to demonstrate the effectiveness of the proposed method. 1DCNN-GRU-ResNet after optimization (1DCNN-GRU-ResNet-opt) achieves 91.6 %, significantly surpassing traditional 1DCNN (82.5 %), GRU (79.9 %), 1DCNN-GRU (85.7 %), and 1DCNN-GRU-ResNet (89.3 %) in the test set. The proposed 1DCNN-GRU-ResNet-opt approach demonstrates considerable potential in practical applications for SHM, offering high accuracy and efficiency.

Railway vibration – fast physics-based models for the prediction of ground vibration and the identification of track damage

The following applications of machine learning/AI for railway vibration will be discussed: 1. The prediction of the wave propagation from a railway line (completely physics based for surface lines, for tunnel lines (partially) physics-based ML for surface lines). 2. The track behaviour for the emission of train-induced ground vibration (physics based for homogeneous soil, ML for layered soil). 3. Track damage detection and quantification from FRF and moving load response. 4. Bridge damage detection and localisation from modal analysis and moving load response. 5. The use of axle-box acceleration for the identification of track/sub-soil condition and bridge resonances. Prediction calculation of railway vibration usually needs time-consuming finite element, boundary element and wavenumber domain calculations. For a user-friendly prediction software, fast calculations are needed. Several time-consuming detailed calculations should be used to develop simpler and fast models. The dynamic stiffness of isolated or un-isolated railway tracks from detailed calculations with a continuous soil is approximated with the simpler Winkler soil. The vehicle-track resonance (P2 resonance) rules the effect of the mitigation measures, and it can also be used for the on-board monitoring of the track and sub-soil condition. For the identification of track damage such as gaps between sleepers, track slabs and layers, detailed models with a continuous soil have been updated to get the best fit to the measured frequency response functions from hammer tests and the deformation pattern from the moving load response. Whereas the track damage can be locally identified, this is more difficult for bridges where the modal analysis gives mainly global information. The influence lines of the inclination for statically passing vehicles (locomotive, truck, compaction ) have been used to localise bridge damage (stiffness variations). The on-board monitoring of rail bridges needs special conditions (regular trains with special speeds) to excite and measure the bridge resonance.

Drive-by bridge damage detection by vehicle-bridge interaction neural operator

Drive-by bridge damage detection using vibrations of vehicles for damage detection of bridges has been studied since it needs no instrumentation on bridges. However, it is still a challenge to be able to extract features that are sensitive to bridge damage and relevant to the responses of the bridge. To address the challenges of drive-by bridge damage detection, machine learning-based approaches have also been actively investigated in recent research. With supervised learning methods, it is almost impossible to obtain training data on the damage state of a bridge. On the other hand, unsupervised learning approaches have been considered, but they only provide changes in relative features rather than physical properties that are useful for estimating the structural performance. To address the above issues, this study aims to propose and investigate the feasibility of drive-by bridge damage detection using a vehicle-bridge interaction neural operator (VINO). In the proposed method, numerical simulations of vehicle-bridge interactions considering the damage field of the bridge are first carried out to generate physically informed training data. The VINO is then constructed using the physically informed training data. However, VINOs are still insufficient to model real bridges. Therefore, in order to improve the practicality of the physically-informed VINO, data-informed fine-tuning is carried out using available data from real bridges. It is noted that no damage data is required in the fine-tuning stage. The feasibility of the drive-by bridge damage detection using VINO is discussed using an in-house experiment on a model bridge under a moving vehicle.

Numerical benchmark for road bridge damage detection from passing vehicles responses applied to four data-driven methods

Drive-by bridge monitoring utilizes measured responses from passing vehicles to perform damage detection of bridge, a methodology challenged by multiple factors and operational conditions. Recently, data-driven methods have been used to improve the accuracy of drive-by monitoring. This thriving research field requires (but lacks) publicly available datasets to improve and validate its monitoring and damage detection capabilities. To foster data-driven drive-by bridge damage assessment methods, this document presents an openly available dataset consisting of numerically simulated vehicle responses crossing a range of bridge spans with various damage conditions. The dataset includes results for different monitoring scenarios, road profile conditions, vehicle models, vehicle mechanical properties and speeds. The intention is to provide a useful resource to the research community that serves as a reference set of results for testing and benchmarking new developments in the field. In addition, four recently published data-driven drive-by methods have been tested using the same dataset.

Influence of the scour phenomenon on the degradation of structures: the case of bridges in Algeria

The scouring of river piles has been the main historical cause of damage and destruction of bridges over the past centuries. Today, the situation is better throughout the world due to modern techniques for carrying out work in the river (foundation work) and the ability to carry out long-distance work, reducing the number of piers in the river. But the scouring of bridge piers remains one of the most frequently recorded degradations in our country. Scouring of bridge piers is a serious issue that can threaten the stability of a bridge. It refers to the erosion of sediment (think sand, gravel, rock) around the base of a bridge pier caused by flowing water. Scour holes can undermine the foundation of the pier and expose the foundation of the pier, reducing its ability to support the weight of the bridge. This can lead to cracks in the pier itself, or even complete collapse in severe cases. On the other hand, scour is the erosion of soil around a bridge pier by flowing water. It's a major concern for bridge safety in Algeria, especially as many bridges were built before stricter seismic codes were introduced in 2010. Scour can weaken the foundation of a bridge pier, making it more susceptible to collapse during earthquakes or floods. The aim of this work is to try to study the phenomenon from different angles in order to clearly define the problem in the Algerian context, based on the analysis of several cases of work subject to scouring.

Hybrid vehicle scanning techniques for detection of damaged hangers in tied-arch railway bridges

Regular monitoring of the hanger system is essential for preserving structural integrity in tied-arch bridges. This proactive monitoring enables preventive maintenance and early detection of hanger damage. To confront this issue, we propose a novel scanning method for assessing the impact of damaged hangers on the dynamic behaviour of single-span tied-arch railway bridges. Using each hanger as a positional reference, this method employs an instrumented vehicle as a mobile scanner to indirectly acquire vibration data from the traversed bridge deck. The hybrid approach integrates vehicle-bridge interaction (VBI) dynamics, vehicle scanning method (VSM), and moving windowed Fourier transform (mw-FT) technique to generate time–frequency spectrograms from the collected signals. In this representation, the time axis indicates the duration of the inspection vehicle traversing the bridge deck. By analysing frequency shifts within the spectrogram, we can identify damaged hangers through observed variations in frequency content over time. Numerical studies demonstrate the efficacy of the proposed hybrid vehicle scanning technique in detecting potential hanger damage in tied-arch railway bridges.