Damage Assessment Of Bridges Research Articles

Damage analysis and assessment of concrete T-girder bridge based on fire scene numerical reconstruction

Fire is a sporadic disaster of concrete bridges, with diverse fire environments and complex damage mechanisms. Accurately evaluating the damage situation of concrete bridges after a fire is exceedingly challenging. This study formulates a damage analysis and assessment method based on the step-by-step and progressively deepening working principle. The method relies on fire scene numerical reconstruction and encompasses key technical aspects, including bridge detection and analysis during the fire incident, fire scene numerical reconstruction, and subsequent bridge damage assessment. Building upon these principles, the study utilizes results from the detection and analysis of the concrete T-girder bridge during a fire incident to establish Computational Fluid Dynamics (CFD) and Finite Element Method (FEM) models for the numerical reconstruction of the fire scene. These models enable the calculation of varying temperature distributions and the evolution of the bridge under fire. Compared with the parameters obtained through the ISO834 method, the numerical reconstruction approaches not only enhances the accuracy of replicating the bridge combustion process but also enables the extraction of temperature field distribution patterns within the bridge fire space and its concrete components.

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.

Seismic performance and damage assessment of bridges during the 2023 Kahramanmaras, Türkiye earthquakes (Mw = 7.8, Mw = 7.6)

This article presents a summary of the damage observed in bridges in the regions affected by the 6 February 2023 Kahramanmaras, Türkiye earthquake sequence. A bridge database was developed based on the observations from multiple reconnaissance groups that visited the bridges. These reconnaissance groups collectively visited 140 individual bridges that were subjected to various intensities of ground shaking. The severity of the observed damage ranged from no damage to total collapse. The types of damage to bridge components mainly included cracking and shifting of abutments, failure of pier cap shear blocks, shifting or dislodging of bearing pads, cracking of girders and loss of prestress, plastic hinging at pier bases, residual pier drift, and distress to deck surfaces, handrails, and carried utilities. Recorded and estimated seismic intensity measures are presented for each bridge site, and statistical information and correlations were developed considering the intensity of shaking, bridge parameters, and observed damage. Observations from a few visited sites are presented as case studies to illustrate the common failure mechanisms. The bridge database and presented results are expected to serve as a reference for further analysis, such as statistical verification, correlation, or damage estimations, and discussion regarding the mitigation of the observed vulnerabilities of bridges in Türkiye and those with similar construction worldwide.

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.

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.

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.

Composite encoder–decoder network for rapid bridge damage assessment using long-term monitoring acceleration data

In recent years, the deployment of structural health monitoring (SHM) systems has become paramount for safeguarding critical infrastructures. Notwithstanding, the development of an unsupervised deep learning framework capable of learning from long-term sensor data remains a critical challenge, particularly in accurately assessing the exact damage location. This study addresses this gap by proposing a novel approach for rapid bridge damage assessment. The proposed method employed a deep overcomplete encoder–decoder network (DOEDN) to reconstruct the acceleration data acquired from each sensor node on the bridge. The reconstruction losses generated by the DOEDN framework are then used as damage-sensitive features. Additionally, a damage indicator based on Gaussian processes is introduced to assess the damage location and evaluate its severity. The performance and sensitivity of the proposed DOEDN framework are evaluated through long-term monitoring acceleration data from a numerical highway bridge model and the well-known full-scale Z24 bridge. Furthermore, comparative assessments against the regular deep undercomplete encoder–decoder network are conducted using metrics including mean absolute error, coefficient of determination (R2), and mean intersection over union. The results show that the proposed DOEDN framework can reasonably assess the damage location and evaluate its severity across various structural scenarios in the bridge, even in the presence of temperature variations, thus providing a practical and effective solution for bridge health monitoring.

Steel truss bridge damage assessment using kurtosis, FFT, spectrogram and Wigner-Ville distribution techniques

Steel truss bridge damage assessment using kurtosis, FFT, spectrogram and Wigner-Ville distribution techniques

Probabilistic autoencoder-based bridge damage assessment using train-induced responses

Structural health monitoring (SHM) systems have been increasingly employed to continually assess the current state of bridges. However, the vast amounts of sensor data generated by SHM systems, along with constantly changing environmental and operational conditions, make structural damage assessment a computationally demanding and challenging task. Traditional data-driven approaches primarily utilise machine learning methods for pattern recognition and feature extraction to address this issue. This paper introduces a methodology for assessing bridge conditions using a probabilistic temporal autoencoder (PTAE). The proposed approach effectively extracts features and captures temporal relationships in multi-sensor data collected only during train crossings. By calculating the reconstruction loss and KL divergence-based of damage features, the methodology enables the identification of potential damage of a monitored bridge. An Exponentially Weighted Moving Average (EWMA) filter and a control chart-based threshold mechanism are applied to further refine the damage assessment process, facilitating the distinction between healthy and progressively deteriorating damage cases. The proposed method is adaptable to various monitoring scenarios and sensor configurations, and robust to varying operational and environmental conditions. The effectiveness of the methodology is assessed using numerically generated data and validated with real-world data from the KW51 bridge. The results demonstrate that the proposed method can detect damage with a limited number of sensors, making it a valuable approach to enhance bridge safety.

Unveiling out-of-distribution data for reliable structural damage assessment in earthquake emergency situations

To ensure accurate and effective emergency responses following an earthquake, one must promptly and accurately assess damage to structures with minimal manual effort. An advanced approach for evaluating structural damage is to use machine learning techniques for automated tagging or damage classification. However, most previous studies evaluated models using simple and unrealistic datasets limited to in-distribution (ID) datasets for training and testing. Although this approach may yield satisfactory results within the confines of the training dataset, it is not justifiable for real-time scenarios, in which the testing dataset may differ. Hence, a novel methodology is proposed herein that focuses on the detection of out-of-distribution (OOD) data. By subjecting a network to outliers, the model can effectively identify data outside the domain of the training dataset. A custom loss function is adopted, where both the cross-entropy loss from the ID training dataset and the log loss from the outlier dataset are incorporated. The effectiveness of this approach is demonstrated through its application in the post-event rapid damage assessment of bridges and shear walls subjected to seismic loading. A single network is employed to classify ID data into their respective classes, whereas the OOD data are shown to belong to the OOD class. The results highlight the significant accuracy achieved in the simultaneous prediction of ID and OOD data. By incorporating the detection of OOD data, this study enriches the methods for improving the reliability and accuracy of structural damage assessments in earthquake emergency responses, thus enabling more informed decision-making.

Rapid damage assessment of regional beam bridges after earthquake based on simplified models and different seismic inputs

As the hub of the traffic network, the rapid damage assessment of bridges after earthquake is of great significance for post-disaster rescue. Traditional vulnerability-based method can only provide the probability of different damage states of structures under different earthquake intensity indicators, which confuses the selection of post-disaster rescue routes. Therefore, this paper proposes a post earthquake damage assessment technique for regional bridges based on nonlinear dynamic time-history response analysis. First, a simplified mechanical model for a single pier was established for a large number of beam bridges in the region, achieving standardized and batch rapid finite element model establishment. Secondly, using Kriging interpolation method and continuous wavelet transform technology, the surface seismic response of geological drilling points is calculated to obtain the seismic motion history of any bridge site in the region. Finally, using parallel computing technology, nonlinear dynamic time-history response analysis was conducted on the simplified beam bridge model, and the bridge damage status was quantitatively evaluated based on the calculation results. Eighty-five bridges in a specific area are considered as an example to verify the proposed method. The results show that the proposed method has high calculation efficiency and can consider the site characteristics and structural design parameters. Also, the calculation results can better reflect the actual earthquake damage distribution, providing strong technical support for post-disaster rescue.

Vehicle assisted bridge damage assessment using probabilistic deep learning

Vehicle assisted monitoring has shown promising potential for the condition assessment of existing bridges in a road network, by removing practical complications faced in traditional Structural health monitoring (SHM) methods such as traffic interruption and dense deployment of sensors. However, the combination of different measurement sources during vehicle assisted monitoring has not yet been fully explored. This paper aims to evaluate the potential benefit of considering multiple measured responses from various sources, including fixed sensors on the bridge and on-board vehicle sensors. To this end, this paper proposes a Probabilistic Deep Neural Network, a stochastic data-driven framework for damage assessment. This framework enables the combination of vehicle and bridge responses to extract damage sensitive features for the classification of different damage states. In addition, the proposed method estimates the uncertainty of its predictions, providing an indication of the reliability of the result. The proposed method is validated using two numerical based case studies while considering realistic operational conditions, which include temperature oscillations, additional traffic, and measurement noise. The results from this study indicate that combining multiple sensor information results in lower uncertainties in damage detection and localisation. The results also suggest that the proposed method is robust in handling measurement noise and varying environmental conditions.

XImpact: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts.

Bridge strikes by over-height vehicles or ships are critical sudden events. Due to their unpredictable nature, many events go unnoticed or unreported, but they can induce structural failures or hidden damage that accelerates the bridge’s long-term degradation. Therefore, always-on monitoring is essential for deployed systems to enhance bridge safety through the reliable detection of such events and the rapid assessment of bridge conditions. Traditional bridge monitoring systems using wired sensors are too expensive for widespread implementation, mainly due to their significant installation cost. In this paper, an intelligent wireless monitoring system is developed as a cost-effective solution. It employs ultralow-power, event-triggered wireless sensor prototypes, which enables on-demand, high-fidelity sensing without missing unpredictable impact events. Furthermore, the proposed system adopts a smart artificial intelligence (AI)-based framework for rapid bridge assessment by utilizing artificial neural networks. Specifically, it can identify the impact location and estimate the peak force and impulse of impacts. The obtained impact information is used to provide early estimation of bridge conditions, allowing the bridge engineers to prioritize resource allocation for the timely inspection of the more severe impacts. The performance of the proposed monitoring system is demonstrated through a full-scale field test. The test results show that the developed system can capture the onset of bridge impacts, provide high-quality synchronized data, and offer a rapid damage assessment of bridges under impact events, achieving the error of around 2 m in impact localization, 1 kN for peak force estimation, and 0.01 kN·s for impulse estimation. Long-term deployment is planned in the future to demonstrate its reliability for real-life impact events.

Data‐driven rapid damage evaluation for life‐cycle seismic assessment of regional reinforced concrete bridges

AbstractRapid and accurate post‐earthquake damage evaluation of regional reinforced concrete (RC) bridges is a key issue for assessing the seismic resilience of cities and communities. Especially, RC bridges are susceptible to the aggressive environment, which can induce time‐dependent aging effects such as corrosion, and thus, it should be considered in the assessment. This paper presents an approach for regional seismic performance assessment of RC bridges in a life‐cycle context based on machine‐learning techniques. The life‐cycle seismic demand and capacity of the bridges are, firstly, obtained by the elaborated numerical model, which includes the deterioration induced by the aging corrosion effect. Then, the tagging‐based damage state (green, yellow, or red) is easily obtained by comparing the pairs of demand and capacity through machine learning. Four hundred and eighty bridge models are generated to develop the machine‐learning models and the performance of the machine learning models is evaluated. Results show that the extreme gradient boosting (XGBoost) model has the best performance, which has an accuracy of 81% in predicting the damage states. The proposed approach is demonstrated with a single bridge example and bridges in a sample region. It is shown that the machine learning model can accurately predict the post‐earthquake damage states of the single bridge, and it can also rapidly assess the damage states of the bridges in the sample region. Approximately 30% bridges in the sample region will experience damage states shift after 100 years, which highlights the importance of considering the aging effects on the post‐earthquake damage assessment of bridges.

Use of UAS for damage inspection and assessment of bridge infrastructures

The present work focuses on the use of Unmanned Aircraft System (UAS), commonly known as drones, for the inspection and assessment of bridge infrastructures. Recent events, in Italy and abroad, have not only brought into question infrastructure behaviour but also reminded that many ordinarily used bridges and viaducts are reaching the end of their design service life. Lack of appropriate maintenance and monitoring could worsen the situation in many circumstances, further calling for cost-effective countermeasures and tools. With this in mind, a comprehensive UAS-based methodology for the inspection and damage assessment of bridges is proposed. Such approach is applicable not only in the aftermath of a disaster (e.g., after a seismic event) but also routinely, to support disaster risk reduction strategies towards resilient bridge infrastructures. In addition to routine inspections, fast UAS-based evaluation procedures of the extent and severity of damage are described with the objectives to determine whether the transportation infrastructure systems are safe and suitable to use or reopen. Thanks to aerial data acquired by means of remote-controlled multirotor UASs equipped with high-resolution cameras, data processing in the form of a 3D virtual model, as an example, allows useful virtual inspections and provides key input information to structural/numerical modelling and analysis, the latter being part of any quantitative risk analysis and loss assessment framework. The abovementioned aspects of the methodology are examined in detail with the support of in-situ applications and corresponding results meant to showcase lessons learnt and issues still open.

Vehicle Assisted Bridge Damage Assessment Using Probabilistic Deep Learning

Vehicle Assisted Bridge Damage Assessment Using Probabilistic Deep Learning

Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems

Machine-learning has recently gained considerable attention in the earthquake engineering community, as it can map the complex relationship between the expected damage and the input parameters. It is often necessary to understand the reasons for the behavior and predictions of the machine-learning model. This paper addresses this issue through interpretable machine-learning approaches such as partial dependence plots, accumulated local effects, and Shapely additive explanations. The evaluation of these approaches is carried out (1) at a component level by analyzing the shear strength predictions by a machine-learning model and (2) at a regional level through the machine-learning model for the regional damage assessment of bridges in California. The comparison helps to identify (1) the proper implementation of these approaches for the efficient use of machine-learning models and (2) key influential variables and thresholds that govern the prediction of the machine-learning models.

Deep autoencoder architecture for bridge damage assessment using responses from several vehicles

Vehicle-assisted monitoring is a promising alternative for rapid and low-cost bridge health monitoring compared to direct instrumentation of bridges. In recent years, centralized management systems for fleets of heavy vehicles have been adopted in transportation networks for logistics and other applications. These vehicles can be instrumented and easily integrated with the existing fleet management systems to provide information that can be used for bridge health monitoring. In this study, a numerical investigation is carried out to evaluate the feasibility of an indirect bridge monitoring system considering responses from several vehicles under operational conditions. The proposed method uses the vertical acceleration responses from a fleet of vehicles passing over a healthy bridge to train a deep autoencoder model (DAE) for bridge damage sensitive features. It is shown that the error in signal reconstruction from the trained DAE is sensitive to damage, when considering the distribution or results from several separate vehicle-crossing events. The bridge damage is quantified with a damage index based on the Kullback-Leibler divergence that evaluates the change in the distributions of the reconstruction errors. The performance of the proposed method is evaluated for two numerical scenarios of vehicle populations, for different damage cases including the effect of operational uncertainties (road profile, measurement noise, and variability in vehicle properties). The proposed method is also evaluated for more realistic multi-span continuous bridge for different damage cases in the presence of random traffic. The result show that the proposed method can detect damage under operational conditions and that it has the potential to become a new tool for cost-effective bridge health monitoring.

Damage assessment and reinforcement measures of prestress concrete bridge after fire

In order to provide reasonable advice for the maintenance and reinforcement of Bridges after fire, it is necessary to assess the damage of Bridges after fire. In this paper, the damage area of a fire-damaged concrete highway bridge is divided according to the concrete apparent detection, and the damage degree of different parts is determined. Carbonization depth and springback detection were used to determine the area and depth affected by fire. Based on the analysis of the surface fire temperature of components in different areas and the internal temperature of concrete, the degradation of the mechanical properties of concrete and steel bars is further deduced, and the damage assessment of the bridge performance is finally carried out.

Multi-Task Convolutional Neural Network for Bridge Damage Assessment

Multi-Task Convolutional Neural Network for Bridge Damage Assessment