Bridge Damage Research Articles

Damage localization and comfort study of glulam arch bridge inspired by Chinese braided timber arch bridges

ABSTRACT In order to leverage the structural advantages exhibited by traditional Chinese braided timber-arch bridges and corridor bridges, this study aimed to study the dynamic characteristics of braided timber-arch-bridges design. Conduct an in-depth investigation into a glulam arch bridge inspired by the iconic Chinese rainbow bridge. Computational modal and experimental modal analyses are performed on this arch bridge, alongside the establishment of a damage model derived from finite element computations. This model serves for precise localization and monitoring of segmental damage. Additionally, anthropogenic vibration response analysis is conducted to assess the structural comfort of the bridge. Our findings indicate that modal parameters are effective in pinpointing the location of damage in glulam arch bridges. Furthermore, the comfort of the structure can be enhanced through the implementation of Multi-Tuned Mass Dampers (MTMD). Following optimization, the bridge meets the requirements stipulated by existing international codes.

Automated intelligent detection system for bridge damages with Fractal-features-based improved YOLOv7

Automated intelligent detection system for bridge damages with Fractal-features-based improved YOLOv7

Impact of Farm Equipment Loading on Rigid Pavement Performance Using Finite Element Analysis

The increase in agricultural product sales in recent years has led to the use of larger hauling and application equipment to transfer farm productions. This rapid shift in equipment size has raised a concern about their potential to cause significant damage in pavements and bridges. The study reported in this paper (part of a larger pooled fund study initiated in 2007) discusses the impact of farm equipment loading on rigid pavement performance based on Finite Element (FE) analysis. The study considered various types of farm equipment to determine the pavement responses and to quantify their damage on rigid pavement systems. The ISLAB2005 FE pavement response model was employed for numerical modeling and analysis of the test sections subjected to farm equipment loading. The results of FE analysis demonstrated that the rigid pavement damage caused by farm vehicles is governed by their axle weight rather than the gross vehicle weight. The FE analysis also showed that the damage resulting from farm equipment loading coupled with PCC slab curling could have a devastating effect on concrete pavement performance.

Arch bridge damage detection using vibration data and gradient-based optimizer algorithm

Arch bridge damage detection using vibration data and gradient-based optimizer algorithm

Robustness assessment of Muscat coastal highway network (CHN) under multi-hazard scenarios focusing on traffic stability and adaptation measures.

This study critically examines the reliability and resilience of the Muscat coastal highway network (CHN) under the compounded effects of earthquakes and floods, representing interacting multi-hazard scenarios. The analysis utilized fragility functions for both earthquake-induced and flood-induced landslides, integrating these with traffic data for selected highway links to estimate bridge damage and assess CHN functionality in post-hazard conditions. Economic sensitivity analysis revealed a significant increase in costs due to flood-induced landslides, emphasizing the impact of dominant intensity measures on network costs and traffic flow. The analysis categorized Muscat areas into low, moderate, and high resilience based on hazard susceptibility and infrastructure quality, revealing that over 50% of highway links require retrofitting, highlighting the need for enhanced flood management and infrastructure improvements. The resilience assessment highlighted the necessity for targeted retrofitting to mitigate damage and reduce economic losses, particularly for highway links with bridges of high failure probabilities that face prolonged recovery times. The results provide valuable insights for designers, consultants, policymakers, and decision-makers in developing effective post-hazard mitigation strategies for Muscat and similar coastal cities.

Landslide Prediction Validation in Western North Carolina After Hurricane Helene

Hurricane Helene triggered 1792 landslides across western North Carolina and has caused damage to 79 bridges to date. Helene hit western North Carolina days after a low-pressure system dropped up to 254 mm of rain in some locations of western North Carolina (e.g., Asheville Regional Airport). The already waterlogged region experienced devastation as significant additional rainfall occurred during Helene, where some areas, like Asheville, North Carolina received an additional 356 mm of rain (National Weather Service, 2024). In this study, machine learning (ML)-generated multi-hazard landslide susceptibility maps are compared to the documented landslides from Helene. The landslide models use the North Carolina landslide database, soil survey, rainfall, USGS digital elevation model (DEM), and distance to rivers to create the landslide variables. From the DEM, aspect factors and slope are computed. Because recent research in western North Carolina suggests fault movement is destabilizing slopes, distance to fault was also incorporated as a predictor variable. Finally, soil types were used as a wildfire predictor variable. In total, 4794 landslides were used for model training. Random Forest and logistic regression machine learning algorithms were used to develop the landslide susceptibility map. Furthermore, landslide susceptibility was also examined with and without consideration of wildfires. Ultimately, this study indicates heavy rainfall and debris-laden floodwaters were critical in triggering both landslides and scour, posing a dual threat to bridge stability. Field investigations from Hurricane Helene revealed that bridge damage was concentrated at bridge abutments, with scour and sediment deposition exacerbating structural vulnerability. We evaluated the assumed flooding potential (AFP) of damaged bridges in the study area, finding that bridges with lower AFP values were particularly vulnerable to scour and submersion during flood events. Differentiating between landslide-induced and scour-induced damage is essential for accurately assessing risks to infrastructure. The findings emphasize the importance of comprehensive hazard mapping to guide infrastructure resilience planning in mountainous regions.

Research on the fatigue performance of continuous beam bridges with vibration-mixed steel fiber-reinforced concrete

To address the fatigue damage issues in continuous beam bridges under vehicle loads, a method using vibration-mixed steel fiber-reinforced concrete to improve critical vulnerable areas of the bridge is proposed, thereby enhancing the bridge’s fatigue resistance. Fatigue performance and micro electron microscopy tests were designed for vibration-mixed steel fiber-reinforced concrete, analyzing its damage conditions and microstructural changes under 0 to 2 million cyclic loads, and the key mechanical parameters of the concrete were determined. Based on this, a numerical analysis model was established to simulate the fatigue damage of continuous beam bridges under moving vehicle loads. The results show that piers made with vibration-mixed steel fiber-reinforced concrete exhibit a 56.86% reduction in compressive damage compared to conventional piers, a reduction in stiffness damage range, and a 29.35% increase in fatigue life.

Risk Assessment of Bridge Damage Due to Heavy Rainfall Considering Landslide Risk and Driftwood Generation Potential Using Convolutional Neural Networks and Conventional Machine Learning

This study addresses the assessment of bridge damage risks associated with heavy rainfall, focusing on landslide susceptibility and driftwood generation potential. By integrating convolutional neural networks (CNNs) with traditional machine learning methods, the research develops an advanced predictive framework for estimating driftwood accumulation at river bridges—a recognized challenge in disaster management. Concentrating on the Tokachi River basin in Hokkaido, Japan, the research utilizes diverse environmental and geographical data from authoritative sources. The findings demonstrate that the innovative approach not only enhances the accuracy of driftwood volume predictions but also distinguishes the effectiveness of CNNs compared to conventional methods. Crucially, areas prone to landslides are identified as significant contributors to driftwood generation, impacting bridge safety. The study underscores the potential of machine learning models in improving disaster risk assessment, while suggesting further exploration into real-time data integration and model refinement to adapt to changing climate conditions and ensure long-term infrastructure safety.

Post-earthquake functionality and resilience prediction of bridge networks based on data-driven machine learning method

Earthquake-induced bridge damage can disrupt transportation networks, potentially hindering emergency response and post-disaster recovery efforts, and posing public safety risks in affected areas. Rapid and accurate assessment of post-earthquake resilience of bridge networks is crucial for evaluating urban seismic performance. Traditional resilience assessment methods, constrained by complex traffic distribution processes, struggle to quickly evaluate the traffic performance of bridge networks during the post-earthquake recovery period. This paper presents a two-layer stacking ensemble model for predicting the functionality and resilience of bridge networks. The first layer integrates advantages of four base learners, including random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and extreme gradient boosting (XGBoost). The second layer completes regression of functionality based on a support vector machine (SVM). Bayesian optimization and 5-fold cross-validation are employed for hyperparameter tuning of the ensemble model. Finally, the proposed model is validated using the Sioux-Falls bridge network. Results demonstrate that the developed model provides rapid predictions of post-earthquake network functionality and resilience. Additionally, this model can guide post-earthquake repair decisions and assist in optimizing the allocation of regional repair resources.

Damage-Sensitive Feature for Long-Span Steel Box-Girder Suspension Bridges Under Service Loads Based on Long-Term Monitoring

To extract valuable data from long-term monitoring and identify structural damage to ensure the serviceability and safety of bridges, a commonly used method in large bridge maintenance. This article developed a time-domain energy-based method for identifying bridge damage based on long-term structural health monitoring noisy output measurements. This method first calculated an energy threshold within the structural frequency bandwidth of natural vibration at measurement points. Then, the structural damage was assessed by computing the time-domain energy accumulation. The improved procedure of this proposed method involved extracting modal frequencies from the signal for each modal order, determining the structural frequency bandwidth of natural vibration, and applying the square root envelope method to smooth vibration data and mitigate the impact of abnormal signals. Later, the applicability of the proposed method for damage identification was verified by comparing it with conventional modal flexibility and modal curvature methods. Finally, a case study on the Runyang bridge, a supported steel-box-girder suspension bridge with a main span of 1490 m in China, was made for demonstration. The results indicated that the area between 1/4-span and 1/2-span of the bridge was more sensitive to damage under service loads. The method performed well in identifying potential damage locations and assessing vulnerability. The calculation results using the method proposed in this article were consistent with those of traditional methods. The research offers methodological backing for utilizing extensive data monitoring in evaluating structural damage.

Innovative wireless sensing for modal analysis and damage modeling of Petőfi Bridge

The historic Petőfi Bridge (1933), a steel truss structure in Győr, Hungary, exemplifies the vulnerability of truss-type bridges to both local and total collapse. This study introduces an innovative, low-cost, accurate, and scalable wireless sensing system (WSS) for Structural Health Monitoring (SHM), utilizing the Petőfi Bridge as a case study. The research details the architecture and workflow of the system, with experimental validations confirming the accuracy of measured acceleration responses. The main natural frequencies of the bridge were estimated by processing the collected data, showing a strong correlation with reference values obtained through conventional wired systems. A calibrated high-fidelity finite element model analyzed the sensitivity of bridge damage detection indicators. The study explores variations in vertical displacement, and modal frequencies, and validates an approach based on displacement influence lines (DILs). The findings indicate the varying efficacy of these indicators in detecting structural damage, providing critical insights for advancing SHM practices.

Reliability Analysis for Dynamic Behaviour, Stiffness, and Strength of Existing Steel Truss Bridge BH77

Railway bridges are old, especially on the Sumatra island and according to earthquake map on 2017 version show an increased risk, it is necessary to analyze the reliability of dynamic behaviour to extend the bridge life and damage can be detected early. The bridge reliability was assessed in terms of natural frequency, deflection, and internal force . The study was conducted on the BH77 Railway Bridge in Tegineneng-Lampung, a through truss type. Reliability analysis with a non-deterministic approach, using the probability concept, variability used is the dimensions of steel profiles based on fabrication drawings and field measurements. The research uses secondary data, one of which is measurement of the circumference of the steel cross section, that influenced by the paint layer, where the paint thickness sample to correct and obtain the actual dimensions. Dynamic behaviour analysis, consisting of modal analysis, Fast Fourier Transform, time history, and First Order Reliabilty Method. The analysis results showed the effect of steel dimension correction compared to the fabrication drawings did not have a significant effect on the changes in the values of natural frequencies, mode shapes, deflections, and internal forces. The reliability of the dynamic behaviour obtained was 99% at all reviews. Which indicates that the bridge is safe against potential resonance, the bridge stiffness is still high, and the axial+moment capacity is still sufficient against static & earthquake loads, as well as good bridge maintenance. The largest deflection point as a reference for placing strain and vibration gauges on structural health monitoring systems.

Research on damage identification of simply supported bridge based on effect size method for vehicle-bridge coupled vibration

Abstract This paper presents a novel bridge damage identification method employing Cohen’s d as an effect size indicator, predicated on the detection of bridge damage through the coupled vibration between a vehicle and the bridge. By analysing the dynamic response of the bridge as a vehicle passes over, this method effectively extracts the modal parameters of the bridge and facilitates the identification of bridge damage. Numerical models of the bridge under various damage conditions, including no damage, mid-span damage, and damage at the 1/4 and 3/4 span locations, have been constructed to substantiate the efficacy and precision of the effect size as a damage indicator. Furthermore, to address the challenge of accurately identifying damage at boundaries, which is often confounded by boundary effects, a boundary subdivision method has been defined for the detection of boundary damage. Through the implementation of a real-bridge test based on indirect measurement technology, the self-vibration frequency of the bridge was successfully extracted. This empirical data was then compared and analysed against results from ANSYS numerical simulations, thereby validating the practicality and accuracy of the proposed method. In the final analysis, the influence of random traffic flow on the bridge damage identification results was examined. The findings indicate that the vibrations in simply supported beam bridges are intensified due to the impact of random traffic flow, which aids in enhancing the accuracy of damage identification. The introduction of this method provides a new quantitative tool for bridge health monitoring, enabling the rapid and accurate identification of bridge damage without interrupting traffic. It holds significant value for engineering applications in bridge maintenance and safety assessment.

Damage detection for railway bridges using time‐frequency decomposition and conditional generative model

AbstractA novel damage detection model, which utilizes the spatiotemporal characteristics of the acceleration data, is proposed to assess the structural integrity of railway bridges. For this, the measured acceleration data are decomposed into several intrinsic mode functions (IMFs) using the sparse random mode decomposition model. The generated IMFs are subsequently integrated into the enhanced time series conditional generative adversarial network model to identify possible damage in bridges across various frequency bands. The influence of environmental and operational variables (EOVs), particularly temperature fluctuations, was also investigated. The proposed model was verified using both numerical and experimental data from a plate girder bridge. Further validation was conducted using the Z24 bridge dataset, and damage cases under the influence of EOVs were successfully predicted. Throughout the validation process, various anomaly metrics were introduced to establish a threshold value, and a covariance‐based time domain metric was proven to be the most effective in our cases.

Bridge Damage Localization Through Response Reconstruction with Multiple BP-ANNs Under Vehicular Loading

Damage detection is a critical aspect of bridge health monitoring. While data reconstruction has been posited as a promising method for damage detection, its effectiveness in this context has rarely been empirically validated. In this study, we introduce a novel approach to pinpoint potential bridge damage by reconstructing bridge inclination data. For an intact bridge, we selected reference cross-sections and trained multiple Backpropagation Artificial Neural Networks (BP-ANNs) to simulate transfer matrices for inclination between these base sections and other sections of the bridge. These BP-ANNs were then employed to reconstruct inclination data at the same cross-sections on a bridge with artificial damage. We demonstrated that damage localization is feasible through a comparison of the reconstructed and actual measured responses. The theoretical underpinnings of the transfer matrix and the damage localization method were initially elucidated through an analysis of the dynamics of a simplified vehicle–bridge interaction (VBI) system. A series of finite element models were constructed to substantiate the theoretical basis of the damage localization method. Additionally, a large-scale laboratory experiment was carried out to assess the practical effectiveness of the proposed method. The proposed method has been demonstrated to effectively pinpoint the location of potential structural damage. It successfully differentiates between areas in close proximity to the damage and those that are more distant. Compared to existing research, our method does not necessitate prior knowledge of factors such as mode shape functions, traffic conditions, or the constraint of inspecting with a single vehicle. This approach is anticipated to be more convenient for engineering applications, particularly in the development of online monitoring systems, due to its streamlined requirements and robust performance in identifying damage localization.

Centrifuge tests study on settlement and damage modes of bridge approaches using deep-seated slab

The issue of bridge end bumps is a critical concern in the failure of bridge and bridge approaches. A series of novel centrifuge tests utilizing a ring model box were conducted to investigate settlement and its induced damages at the bridge approach. A new mitigation method, the deep-seated slab, for bridge end bumps was modeled in the test. This study analyzed the decisive role of pavement stiffness, soil modulus, and load cycles on deformation from the perspective of structure-soil interaction under standard traffic load conditions. The test results show that when deep-seated slabs are used, the deformation of the bridge approach follows an exponential decay pattern, eventually stabilizing after approximately one slab length. Furthermore, the upper and lower bridges exhibit distinct damage modes, i.e., the bridge damage by wheel collision at the upper bridge and the pavement damage by wheel impact at the lower bridge. The damage zone on the pavement is approximately 1.7 times the wheel width and the damage zone on the bridge 2.6 times. Finally, a predictive model for the deformation of bridge approaches was proposed, considering the effect of pavement stiffness, subgrade soil modulus, and load cycles. The relationship between the deformation and the three normalized variables conforms to the quadratic polynomial function in matrix form.

Effect of Non-Uniform Temperature Distribution on the Natural Frequency of Concrete Girder Bridges

Structural dynamic properties like frequencies and mode shapes have been widely adopted for bridge condition assessment and damage identification. One of the main challenges lies in environmental factors, particularly the varying temperature, which significantly influences the bridge’s natural frequencies. In some cases, the effect of temperature changes can be comparable to, or even exceed, the effect caused by damage, rendering the damage identification methods ineffective. This study investigates the influence of the cross-sectional non-uniform temperature distribution, as a result of surrounding environmental factors, on the frequency of concrete girder bridges. A theoretical analysis is conducted to derive the bridge’s frequencies considering the cross-sectional non-uniform temperature distribution. The upper and lower bounds of the frequencies are developed by using only the environmental temperature and the largest temperature gradient defined by codes. More importantly, the damage to the bridge can be detected if the frequencies exceed the bounds for a certain period. This approach is convenient and practical in real applications without embedding thermocouples in the main girder. Numerical simulations, laboratory experimental tests, and field measurements are carried out to validate the derived frequency considering the cross-sectional non-uniform temperature distribution as well as the upper and lower bounds of the frequency. In particular, the results of the Z24 bridge show that when the bridge is damaged, the measured frequency exceeds the bound continuously, verifying the capability of the developed upper and lower bounds to evaluate bridge conditions.

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.

Enhanced ANN-based ensemble method for bridge damage characterization using limited dataset

Bridges are vital assets of transport infrastructure, systems, and communities. Damage characterization is critical in ensuring safety and planning adaptation measures. Nondestructive methods offer an efficient means towards assessing the condition of bridges, without causing harm or disruption to transport services, and these can deploy measurable evidence of bridge deterioration, e.g., deflections due to tendon loss. This paper presents an enhanced input-doubling technique and the Artificial Neural Network (ANN)-based cascade ensemble method for bridge damage state identification and is exclusively relying on small datasets, that are common in structural assessments. A new data augmentation scheme rooted in the principles of linearizing response surfaces is introduced, which significantly boosts the efficiency of intelligent data analysis when faced with limited volumes of data. Furthermore, improvements to a two-step ANN-based ensemble method, designed for solving the stated task, are presented. By adding the improved input-doubling methods as simple predictors in the first part of the cascade ensemble and optimizing it, we significantly boost accuracy (7%, 0.5%, and 8% based on R2 in predicting tendon losses for three critical zones that were defined across the deck of a real deteriorated prestressed balanced cantilever bridge). This improvement is strong evidence of the accuracy of the proposed method for the task at hand that is proven to be more accurate than other methods available in the international literature.

Enhancing bridge damage detection with Mamba-Enhanced HRNet for semantic segmentation.

With the acceleration of urbanization, bridges, as crucial infrastructure, their structural health and stability are paramount to public safety. This paper proposes Mamba-Enhanced HRNet for bridge damage detection. Mamba-Enhanced HRNet integrates the advantages of HRNet's multi-resolution parallel design and VMamba's visual state space model. By replacing the residual convolutional blocks in HRNet with a combination of VSS blocks and convolution, this model enhances the network's capability to capture global contextual information while maintaining computational efficiency. This work builds an extensive dataset with multiple damage kinds and uses Mean Intersection over Union (Mean IoU) as the assessment metric to assess the performance of Mamba-Enhanced HRNet. Experimental results demonstrate that Mamba-Enhanced HRNet achieves significant performance improvements in bridge damage semantic segmentation tasks, with Mean IoU scores of 0.963, outperforming several other semantic segmentation models.