Bridge Maintenance Research Articles

Resource-constrained bridge maintenance optimization by harmonizing structural safety and maintenance duration

Bridges are progressively experiencing significant structural wear and challenges in their maintenance procedures. To address this issue, it's crucial to clarify the order of maintenance activities during the bridge maintenance process and the impact of maintenance resources on structural safety performance. This understanding allows for the precise establishment of a model that captures the changes in structural safety performance in relation to the bridge maintenance duration that are resource-constrained. This paper proposes a resource-constrained bridge maintenance optimization method where neither the structural safety performance loss nor the duration holds a dominant advantage. An Adjusted NSGA-II (Adj-NSGA-II) method is employed to determine the total structural safety performance loss and total duration throughout the bridge's operational cycle. The results show that the number of construction workers in the mathematical model proposed will have different degrees of influence on the change of the total construction duration under the similar total structural safety performance. The proposed method not only enhances the scientific management capabilities of decision-makers in bridge maintenance planning but also provides a framework that can be applied to maintain other structural forms.

Study on Vehicle-Bridge Coupled Vibration of Curved Girder Bridges under Different Road Surface Grades

This study explores the impact of different levels of road surface smoothness on the dynamic response of bridges. By conducting a detailed analysis under five different conditions of road surface smoothness, the mechanisms by which road surface irregularities affect the dynamic characteristics and safety performance of bridge structures are revealed. The research employs the finite element simulation method to compare and analyze the changes in key dynamic indicators such as vertical displacement, torsional angle, torque, and bending moment of bridges under different grades of road surface smoothness. The results indicate that a decrease in road surface smoothness significantly increases the dynamic response of bridges. The findings provide important reference information for bridge engineers in the design and maintenance of bridges.

Evaluating the Delivery and Oversight of State-Funded Local Bridge Projects

State-funded local bridge projects face several challenges including but not limited to constrained budgets, increasing burden from state departments of transportation (DOT) staff, and the lack of expertise of some local personnel. By streamlining the delivery and oversight of state-funded projects through delegation, a collaborative and proactive approach can be achieved, ensuring the successful maintenance and development of local low-risk bridges, which are not on the National Highway System and have minimal to no environmental, right of way, utility, or railroad impacts. This paper offers a practical approach to evaluating programs focused on progressing project delivery by delegating specific tasks and responsibilities to local governments. Taken as a study case, Wisconsin Department of Transportation’s low-risk bridge pilot program was evaluated using both a quantitative methodology: data analysis using a data-driven statistical approach involving analysis of pilot and control group data, and an evaluation of the performance metrics developed by the state DOT; and a qualitative approach: a survey of local sponsors and stakeholders about the pilot project. The survey corroborated the findings derived from the data analysis: improved performance in project cost and schedule for pilot projects, highlighting consistency and reliability in the evaluation process. The integration of both methodologies resulted in valuable insights and experiences, providing valuable guidance to other states conducting similar program evaluations.

Research on Quality Inspection and Reinforcement Technology of Road Bridge

With the acceleration of urbanization, road bridges play a crucial role in urban transportation, and their quality condition is directly related to traffic safety and urban development. The article analyzes the common problems of existing road bridges, mainly including cracks, settlement problems and other aspects, to provide a reliable basis for the maintenance and reinforcement of road bridges. Based on this, the article mainly researches the quality inspection technology of road bridges and the reinforcement technology of old bridges at the present stage, and analyzes the application of quality inspection technology and reinforcement technology in improving the structural quality of road bridges according to the construction cases of Jialingjiang Bridge, Qiuyun Bridge and Luoxi Bridge in Heyang, so as to improve the safety of road bridges and prolong the service life of bridges.

GBC-BCD: an improved bridge crack detection method based on bidirectional Laplacian pyramid structure with lightweight attention mechanism convolution

ABSTRACT As roads continue to expand globally, the construction of a larger number of bridges has significantly increased the burden on bridge maintenance. Traditional methods for bridge crack detection often suffer from low efficiency and accuracy. To address these issues, we propose an efficient one-stage bridge crack detection method called GBC-BCD based on the YOLOv8n framework. To improve the model’s performance, we introduce the Coordinated Attention mechanism to enhance spatial context awareness and the Bidirectional Feature Pyramid Network to strengthen the ability of feature fusion and multi-scale feature learning. At the same time, in order to make the model more efficient while improving its performance, we introduce the Ghost Module, which significantly reduces the size of the model and raises the speed of object detection. Moreover, we employ transfer learning to improve training efficiency and conserve computational resources for small datasets. Experiments show that our model has the characteristics of lightweight and high speed. Compared with famous lightweight object detection algorithms, the model size is at most reduced by 86.6%, and realises real-time (625 FPS) processing of images ( 640 × 640 ), an 8.3 times increment in speed.

Stochastic Vibration-Driven Fatigue Analysis for Steel Structure Bridges Considering Track Irregularity

This study proposes a method for assessing the effect of stochastic vibrations resulting from track irregularities which are rarely studied but significant on bridge fatigue. First, the pseudo-excitation method is utilized to determine the power spectrum density of stress. Subsequently, the trigonometric series method is employed to acquire the stress time history set. Then, a traditional fatigue life calculation method is employed for analysis. By utilizing a post-sampling method, the need for costly repetitive solutions of the vehicle-bridge coupling system is eliminated. Numerical simulations are conducted on a steel truss bridge, revealing the non-negligible effect of track irregularity on bridge fatigue performance. The study investigates the underlying mechanism, highlighting the pronounced influence of long-wave track irregularity. Moreover, the research explores the correlation between track smoothness and bridge fatigue performance, demonstrating that a decrease in track smoothness leads to an increase in fatigue loss. These findings emphasize the necessity of considering stochastic vibrations when evaluating bridge fatigue, providing valuable insights for bridge design and maintenance.

Modeling life expectancy and cost effectiveness for UHPC bridge retrofitting techniques

Bridge components are subject to both structural loads and environmental stressors, rendering them susceptible to accelerated deterioration and potential collapse in the absence of effective maintenance and rehabilitation strategies. Moreover, the phenomenon of wet-dry cycling, coupled with elevated chloride concentrations prevalent in coastal regions, further expedites the degradation process of bridges, thereby escalating maintenance frequency and repair costs. In response to this challenge, the integration of innovative materials such as Ultra High-Performance Concrete (UHPC) is being explored for the development and implementation of maintenance and rehabilitation strategies. This study presents a comparative analysis between conventional methods and UHPC applications for bridge repairs, utilizing Life Cycle Cost Analysis (LCCA) to encompass both agency and user costs, and applies Monte Carlo simulation to account for the variability of the modeling factors. A practical case study illustrates the applicability of the LCCA methodology, revealing that the utilization of UHPC contributes to a reduction in the total life cycle cost for bridge maintenance and rehabilitation. Life expectancy, Average Daily Traffic (ADT), and the duration of construction activities during rehabilitation emerge as the most influential factors affecting life cycle costs. The main contributions of the study are the development of the life-expectancy model and step-by-step Life-Cycle Cost Analysis (LCCA) methodology. Findings from this study aim to identify cost-effective retrofitting techniques for maintaining bridges in a “State of Good Repair.”

Machine Learning–Based Bridge Maintenance Optimization Model for Maximizing Performance within Available Annual Budgets

Machine Learning–Based Bridge Maintenance Optimization Model for Maximizing Performance within Available Annual Budgets

Investigating the Application of Smart Materials for Enhanced Maintenance of Rubber Expansion Joints in Bridge Expansion Joint Systems

In South Korea, the public infrastructure encompasses 172,111 facilities, with bridges accounting for a significant segment (totaling 34,199). These bridges undergo expansion due to traffic, vehicular loads, and temperature fluctuations. Expansion joint devices are installed to maintain vehicle stability and driving performance across expansion gaps. While these devices effectively ensure vehicular stability and performance, they do not address issues such as leakage and debris fall; therefore, rubber expansion joints should be installed. However, these rubber joints are prone to damage from various factors, resulting in secondary issues such as girder corrosion and accidents under bridges. Because of these inherent vulnerabilities, these joints require frequent replacements, making continuous bridge maintenance challenging. Therefore, this study explores the development of novel expansion joints using superelastic shape memory alloys to overcome the limitations of traditional rubber expansion joints. A comparative finite element analysis was conducted on the developed superelastic shape memory alloy and traditional rubber expansion joints. This study also assessed the long-term usability of these novel joints, particularly their ability to revert to their original shape post load removal. This research presents a promising alternative to conventional expansion joints and holds potential implications for enhancing the durability and safety of bridge infrastructure.

Supervision of Road Maintenance Implementation at the Self-Managed Technical Implementation Unit (UPTD) Road and Bridge Maintenance of Public Works and Spatial Planning Agency of Banjarmasin City Government

This study evaluates the supervision of road maintenance implementation by the government, with the aim of identifying inhibiting factors and proposing efforts to overcome them. The research approach was qualitative, with the Head of the Road Division as the key informant and Section Heads and staff as additional informants. Data collection methods included observation, interviews and documentation, and data were analyzed using an interactive analysis model. The identified inhibiting factors include the lack of apparatus quality, inadequate work facilities and infrastructure, insufficient road supervision funds, not optimal utilization of apparatus resources, and inappropriate apparatus placement. Efforts made to overcome these factors include improving the quality of human resources, improving facilities and infrastructure, allocating larger budget funds, implementing supervision programs based on needs analysis, making officer development plans, and evaluating project supervisory employees. By overcoming these inhibiting factors and implementing these efforts, it is expected to increase the effectiveness of road maintenance supervision and provide better road conditions for the community

Digital simulation of distortion-induced fatigue in steel bridges with different geometrical configurations

Distortion-induced fatigue cracks at web gaps often occur in multi-girder steel bridges under vehicle loads. This paper presents full size digital simulations of steel girder bridges with different geometrical configurations, to explore the distortion-induced fatigue cracking performance. In the digital simulations, four geometrics are considered, in addition to two web gap details, namely the vertical stiffener web gap and the horizontal gusset plate web gap. Introducing multi-layer and multi-pass welds residual welding stress fields, digital fatigue simulation results show that the distortion-induced fatigue cracks at both web gaps are Mode I-II-III combined cracks dominated by Mode I. The coupling of the three modes is more prominent at the vertical stiffener web gap compared to the horizontal gusset plate web gap. In the skewed bridge, curved bridge, and skewed-curved bridge, the significant bending-torsional coupling effect, the structural asymmetry, and the stress deformation asymmetry cause an increment in distortion ranges, fatigue crack growth rates, and the proportions of Mode II and III cracks. The fatigue strength categories for vertical stiffener web gap and horizontal gusset plate web gap in straight, skewed, curved and skewed-curved bridges are category 125, 112, 110 and 110, and category 160, 140, 100 and 100 of the Chinese specification and Eurocode, respectively. Therefore, the geometrical configuration is an important factor affecting the distortion-induced fatigue behavior, which should be considered in the fatigue design and maintenance of steel girder bridges.

Shrinkage and creep effect analysis of beam-arch composite bridge based on field test

To study the time-dependent behavior of the beam-arch composite bridge under the effects of shrinkage and creep, this paper conducted field tests on C55 concrete with different reinforcement ratios at the bridge site environment. Based on the experimental results, the commonly used CEB-FIP-90 model in engineering was modified, and new shrinkage strain and creep coefficient values were proposed. These modifications allowed for an analysis of the long-term performance of the bridge. Finally, the influence of the arch rib stiffening on long-term performance was discussed. The results indicate that the modified model aligns better with the actual structure compared to the CEB-FIP-90 value. During the operation stage, the bridge exhibits an overall significant compressive stress reserve. Specially, it is worth noting that the concrete footings experience tensile stress, which indicates a risk of cracking during the operation stage. Furthermore, the deflection of the beam-arch composite bridge is 1/7 of that of the continuous rigid frame bridge while the stress distribution in the beam-arch composite bridge is more uniform. This paper provides important reference value for the maintenance and management of beam-arch composite bridges during their operation period.

Optimized Bridge Maintenance Strategies: A System Reliability–Based Approach to Enhancing Road Network Performance

Optimized Bridge Maintenance Strategies: A System Reliability–Based Approach to Enhancing Road Network Performance

Assessing and Improving Maintenance and Safety Inspection Policy for Suspension Bridges in Gyeonggi-do Through On-site Investigation

Assessing and Improving Maintenance and Safety Inspection Policy for Suspension Bridges in Gyeonggi-do Through On-site Investigation

Optimized deep learning for steel bridge bolt corrosion detection and classification

Corrosion of steel bridge bolts poses a major threat to structural integrity. As a key component, bridge bolts are susceptible to corrosion and detachment, necessitating close monitoring during maintenance. However, the vast number of corroded bolts presents challenges. Conventional detection relies on subjective visual inspection without unified assessment criteria. Fast and accurate localization and classification of bolt corrosion levels thus became a research priority for bridge maintenance. This study puts forward a new two-stage corrosion detection method, integrating Deep Convolutional Generative Adversarial Network (DCGAN) and improved You Only Look Once version 5 (YOLOv5) networks, which enables rapid categorization of bridge bolts by different corrosion extents, providing an intelligent solution for bridge maintenance. First, in the image preprocessing stage, a DCGAN with least squares loss generates simulated corroded bolt images through adversarial learning to expand the dataset. Second, three corrosion levels are defined based on visual features and percentage of surface rust, with this classification scheme applied in dataset annotation. Finally, the annotated images are input into an enhanced YOLOv5 model named YOLOv5-SE for corrosion identification. To improve accuracy, Squeeze-and-Excitation blocks are added after each residual block in the YOLOv5 backbone. Comparative experiments prove YOLOv5-SE's superior performance over YOLOv5 and other models like Faster R-CNN and VGG16. A pytorch system enables model deployment for corrosion detection. The integration of DCGAN simulation and attention-optimized YOLOv5-SE enables precise classification and localization of corroded bolts. This study represented a significant step towards intelligent upgrades in steel bridge maintenance.

A framework for decarbonisation for bridge owners, engineers and designers

The Net Zero Bridges Group brings together bridge specialists concerned to lower the carbon dioxide footprint of their work, and to understand collectively how best to do so. Achieving net zero carbon dioxide by 2050 is the best way to mitigate the severity of the climate emergency. In addition to social consequences, failure will make ensuring the climate resilience of transport infrastructure increasingly difficult. Bridge engineers must rapidly develop new knowledge and skills, and commit to gradual but steady decarbonisation compatible with agreed climate pathways and timelines. Prioritising decarbonisation requires making different decisions to those traditionally made. The carbon dioxide reduction hierarchy asks that measures be prioritised to ‘avoid, switch and improve’. Applying these to bridge engineering requires bold choices, while managing risk effectively. The project life cycle must be rethought to allocate sufficient time at the right time. Above all, the challenge requires collaborative, collective action, to share data and best practice, rather than working in isolation. This paper sets the above in an overriding framework for action, and provides an overview of current thinking for decarbonising bridge construction and maintenance. It identifies priorities for the industry to tackle within the next few years.

Research on an Intelligent Identification Method for Cable-Stayed Force with a Damper Based on Microwave Radar Measurements

Aiming at the shortcomings of traditional contact cable force monitoring technology in accuracy, efficiency, and applicability, an assessment method based on microwave radar measurements is proposed to measure a sloping cable with a damper for lengths greater than 200 m in this study. A formula for calculating the cable-stayed force with a damper is derived, and an intelligent cable force monitoring platform is developed based on cloud technology. Based on the Chongqing Nanjimen Railway Bridge, a real bridge test was carried out. It was indicated that the microwave radar method could be used to freely adjust the measurement angle and possessed high applicability and penetration. It significantly improved the measurement accuracy and efficiency of cables with a damper for lengths greater than 200 m. It has good application value for the solution of the problems of complicated operation and high costs in the monitoring of cables with a damper. The formula for calculating the cable force with a damper was proven to be reliable and accurate when compared to the results of direct calculation and the equivalent cable length method. It was able to significantly reduce the calculation error of the cable force caused by the influence of the damper. Additionally, the intelligent cable force monitoring platform was utilized to enhance the level of digitization, providing technical support for the scientific management and maintenance of bridges.

Study on corrosion performance and corrosion product composition of weathering steel and high‐performance steel in simulated C3 environment

AbstractWeathering steel (WS) and high‐performance steel (HPS) have been widely used in bridge engineering. Investigating the corrosion performances of WS and HPS is of great significance to the safety evaluation and maintenance of uncoated WS or HPS bridges, especially aging bridges. This study focused on the correlation between the corrosion performance and composition of the rust layer. Accelerated corrosion tests were conducted on Q345CNH and HPS 70 W to observe their corrosion performance under salt‐spray corrosive environment. Based on the results of corrosion test and the existing corrosion attack evaluation theories, the empirical formulae of uniform corrosion and pitting corrosion were derived. X‐ray diffraction experiments were performed to analyze the composition of the rust layer formed on the surface of steel specimens after various corrosion cycles. The relationship between the composition of the rust layer and the corrosion performance was established. The test corrosive environment was verified to be consistent with the C3 environment. The conclusions of this study could provide guidance for the corrosion evaluation in the following test or practical application.

Structural health monitoring of a lenticular truss bridge: a comprehensive study

The combined use of finite element modeling and structural health monitoring is becoming increasingly relevant in bridge maintenance. A growing number of new and old structures is instrumented with different kinds of sensors driven by dedicated hardware and software, while numerical models are used to simulate the response of the structures under various scenarios. This paper presents the monitoring of the Smithfield Street Bridge, one of the oldest and most iconic bridges in the city of Pittsburgh (Pennsylvania, USA). The bridge was instrumented by an independent party, not involved with the research presented here, with strain, displacement, and rotation wireless sensors. A detailed finite element model of one of the spans of the bridge was developed to calculate the strains induced by standardized trucks under pristine and simulated damage conditions. In addition, the model enabled to determine the sensitivity necessary to detect relevant structural changes in the bridge. The results of the numerical analyses were then compared with the results of a test in which a truck of known weight crossed the bridge multiple times. Finally, the data relative to 3 years of uninterrupted monitoring were processed and analyzed to identify eventual anomalies.

Condition Rating Prediction for Highway Bridge Based on Elman Neural Networks and Markov Chains

Bridges are a critical component of transportation infrastructure, playing a vital role in connectivity. The safe operation of bridges demands significant resource and capital investment, particularly as the operation phase is the most extended period in a bridge’s life cycle. Therefore, the efficient allocation of resources and funds is crucial for the maintenance and repair of bridges. This study addresses the need to predict changes in bridge condition over time. The commonly used state-based Markov chain method for bridge condition rating prediction is straightforward but limited by its assumptions of homogeneity and memorylessness. To improve upon this, we propose a novel method that integrates an Elman neural network with a Markov chain to predict the bridge condition rating. Initially, the ReliefF algorithm conducts a sensitivity analysis on bridge features to obtain the importance ranking of these features that affect the bridge condition. Next, six significant features are selected for data classification: bridge age, average daily truck traffic volume, material type, skew angle between bridges and roads, bridge deck structure type, and bridge type. The Elman neural network is then trained to train a prediction model for bridge condition ratings using the classified data, which can predict the condition levels of bridges. The Markov chain’s transition probability matrix is derived using a genetic algorithm to match the deterioration curve predicted by the Elman neural network. This proposed method, when applied to actual bridge data, demonstrates its effectiveness as evidenced by the condition rating of an actual bridge.