Bridge Management Research Articles

Bridge Assessment Analysis using Sufficiency Rating Method (Case Study of The Mungkung Overpass)

Bridges play a crucial role in connecting various routes and water bodies, necessitating regular monitoring to prevent potential hazards and losses due to deterioration. In Indonesia employs the Bridge Management System (BMS) for decision-making regarding maintenance, repair, and replacement activities, its reliance on a limited set of criteria may lead to uncertainties in bridge condition assessmsents. This study explores the Sufficiency Rating (SR) method as an alternative for assessing bridge feasibility. By considering multiple factors, including structural adequacy, serviceability, functionality, and public interest, the SR method offers a comprehensive approach to bridge evaluation. An analysis of the Mungkung Overpass on the Ngawi Kertasono toll road was conducted to evaluate the effectiveness of the SR method. The findings classified the bridge as excellent, indicating no immediate need for treatment. However, the SR method has limitations, particularly in visually assessing damage to superstructure and substructure elements, which may introduce subjectivity. Overall, this research contributes to the ongoing development of bridge assessment methodologies in Indonesia, aiming to enhance decision-making processes and ensure the safety and longevity of bridge infrastructure. After analyzing the data obtained through the implementation of the SR method, it was revealed that S1 achieved a value of 45%, S2 achieved 23%, S3 achieved 12.7%, and S4 achieved 6%. The total actual value of the four parameters calculated based on the SR method was 74.7%, with a difference of 25.3% from the maximum value. These findings clearly illustrated that the bridge satisfied the criteria for deficiency, thereby necessitating comprehensive rehabilitation measures to be undertaken.

Dynamic construction of visible knowledge graph based on 3D geographic scene for intelligent bridge management

Dynamic construction of visible knowledge graph based on 3D geographic scene for intelligent bridge management

Evolution of Digital Twin Frameworks in Bridge Management: Review and Future Directions

Over the last decade, the digital twin (DT) concept has effectively revolutionized conventional bridge monitoring and management. Despite their overall success, current bridge DTs encounter conceptual ambiguities, hindering their inherent potential for practical implementation. Moreover, intelligent decision support models have not been properly considered as a component of the bridge DTs framework to enhance the reliability of decisions for asset maintenance. Therefore, this paper conducts a scientometric analysis and a comprehensive state-of-the-art review, exploring current bridge DT research trends and architectures and introducing an enhanced conceptual framework for bridge DTs. To this end, more than 480 research publications have been reviewed, compared, and analyzed. The research result encompasses the redevelopment of a multilayer DT framework, fostering its implementation in the full lifecycle of bridge infrastructure while exploring the potential integration of decision support systems and data fusion from advanced technologies to improve the overall efficiency of implementing DT technology in bridges.

Using machine learning algorithms for predicting bridge vertical deflection

Machine learning (ML) is being increasingly used to ease structural health monitoring and management of bridges. Therefore, the main objective of this study is to apply two state-of-the-art ML algorithms, namely locally weighted learning (LWL) and K-Star for the prediction of vertical deflection of composite bridges. To accomplish the objective, 83 track loading tests were carried out at various bridges located in Vietnam and deflection data was collected for models’ development. Model's validation and comparison were carried out using different popular methods, namely mean absolute error, root mean squared error and R on both training (70%) and validation (30%) datasets. The results of this study indicate that the K-Star algorithm outperforms LWL in predicting the vertical deflection of composite bridges. Consequently, K-Star can serve as an effective tool for rapidly predicting vertical deflection, thereby facilitating more efficient bridge health monitoring and management. This efficiency can lead to significant time and cost savings in maintaining the structural integrity of composite bridges.

Transfer from defective bridge information model to defect analysis model based on industry foundation classes

Bridges, as an important part in modern infrastructure networks, suffer from increasingly severe aging issues in recent years. Road agencies and authorities conduct regular inspections for their bridges and identify defects and deteriorations for condition assessment. Such defect information cannot be efficiently used for analysis in the structural domain. This study proposes an Industry Foundation Classes-based method to allow for the transfer from a defective bridge information model to a defect analysis model. The architectural model of bridge is first converted into analytical model and then the impacts of defects on structural properties are quantified with a stiffness reduction coefficient. Case studies are conducted to validate the applicability of the proposed method for defect analysis and a range of coefficients are tested on an illustrative case bridge. The results prove that the proposed method can generate instructive and informative knowledge on the safety and reliability of the entire structure. This study narrows the gap between the empirical condition assessment scheme and the numerical structural analysis scheme for bridge management and manages to make full use of inspection-related information within Building Information Modelling (BIM) environment.

СТАН МОСТОВОГО ГОСПОДАРСТВА УКРАЇНИ НА ДОРОГАХ ЗАГАЛЬНОГО КОРИСТУВАННЯ ДЕРЖАВНОГО ЗНАЧЕННЯ НА ПІДКОНТРОЛЬНИХ ТЕРИТОРІЯХ ЗА 2023 РІК

Introduction. Bridges are important critical infrastructure facilities; they play a key role in economic development and ensure the security of regions and the state as a whole. During the period of martial law, bridges became an important element of the country's defense capability, as well as routes for humanitarian evacuation and export logistics. To make effective management decisions at the level of the State Agency for the Restoration and Development of Infrastructure of Ukraine in the field of bridge management, the Analytical Expert Bridge Management System (AESUM) software package is being implemented. Problem statement. In connection with the introduction of martial law in Ukraine, ensuring the proper functioning of the bridge sector has become of great social importance, since the availability of sustainable transport links allows to ensure the vital activity of the population and the functioning of the state under martial law. It is also important to improve the strategy of operation of transport facilities, taking into account the scientific and analytical apparatus of the AESUM software package. Objective. Implementation of an effective strategy for the operation of bridges on highways in order to plan financial resources for their repair and maintenance. Providing the general public with up-to-date statistical information on bridges from AESUM will be useful for scientific and practical purposes, and identifying certain analytical trends will allow to improve AESUM in terms of forming a strategy for the operation of transport facilities. Materials and methods. Analytical, statistical, system analysis. Results. The paper presents analytical and statistical information on transport facilities on public roads of national importance in the controlled territories of Ukraine as of the end of 2023. Certain trends have been identified and the technical condition of transport facilities has been focused on.

Impact of Environmental Conditions on Predicting Condition Rating of Concrete Bridge Decks

Highway agencies prioritize maintaining bridge infrastructure through bridge management systems amid budget constraints. The premature deterioration of reinforced concrete (RC) bridge decks caused by more frequent and increasingly heavy truck load spectra coupled with aggressive environmental conditions has become a critical concern. Despite the prevalence of conventional models and the emerging popularity of machine learning (ML) models in bridge deterioration predictions, they fall short in feature selection and handling of climate conditions, leading to suboptimal accuracy. To address these gaps, this study presents a data-driven framework utilizing ML-based techniques to predict the condition rating of RC bridge decks with a focus on identifying the influencing factors that affect the deck condition. The framework employs the XGBoost algorithm for model development, encompassing comprehensive datasets that include structural, geographical, and climate variables from across the U.S. Furthermore, the Shapley additive explanations approach is applied to identify the explanatory variables with the most impact. Age emerged as the most crucial factor, followed by freeze-thaw cycles and truck traffic, as indicated by the average daily truck traffic. Rainfall also plays a substantial role in deck deterioration. Based on feature importance and monotonicity, this study recommends a series of bridge classifications for transportation agencies to incorporate into their deterioration models. Overall, this research enhances understanding of the primary causes of bridge deck deterioration, enabling more informed decisions about funding allocation and bolstering bridge performance against environmental challenges.

An Analytical Study of Suspension Bridge Collapse Based on the Characteristics of the Eigen Values

This research discusses an analytical approach to suspension bridge accidents focusing on eigenvalue characteristics. This research uses eigenvalue analysis to explore patterns that may contribute to bridge structural failure. The analysis steps include reducing the model to an uncoupled and linear system, as well as determining homogeneous and non-homogeneous solutions, which helps identify accident-prone patterns. The analysis results also reveal significant differences in dynamic characteristics between safe bridges and those that experience accidents. This research highlights the importance of a deep understanding of eigenvalues in designing, maintaining, and managing suspension bridges. The implication of this research is the development of more effective prevention strategies to improve suspension bridge safety in the future. By analyzing eigenvalues, this research provides deep insight into the factors that influence the stability of suspension bridges. These findings provide a strong foundation for developing recommendations for more effective design, maintenance, and management of suspension bridges. The conclusions of this research can help improve the safety and reliability of suspension bridges worldwide and contribute to civil engineering and infrastructure security.

Bayesian Decision-Making Process Including Structural Health Monitoring Data Quality for Bridge Management

Bayesian Decision-Making Process Including Structural Health Monitoring Data Quality for Bridge Management

Kajian Pemeriksaan Kondisi Jembatan di Ruas Jalan Nasional Provinsi Jambi (Studi kasus: Jembatan Rangka)

Bridge inspection is one of the most important parts in a bridge management information system. Bridge inspection is related to bridge requirements, planning for maintenance or service improvement in the future. This study discusses the framework of bridges on several bridges which are stretched on Jambi provincial roads, specifically the Sei Bridge. Bernai, Lubuk Kuari and Sei Bridge. Trap Expected more than 25 years. The examination in this study only waits for the detailed inspection of the manual issued by the public works department. This study uses the calculation method proposed in the BMS (Bridge Management System) which has been translated into SMJ (Bridge Management System) based on conditions that occur in the field. The results of the bridge situation examination obtained from this study are Sei. Trapping has a condition value of 1, Lubuk Kuari has a condition value of 2, Sei. Bernai has a condition value of 1.

A risk-based quantitative approach for priority assessment of ageing bridges accounting for deterioration

The significant role of bridges in transportation networks requires continuous improvement of bridge management strategies, which has been even more exacerbated by the recent bridge collapses worldwide. This study proposes a framework for the prioritization and preliminary risk assessment of existing bridges subjected to seismic and traffic hazards. The proposed method adopts quantitative expressions to define risk in a simplified manner and, consequently, the priority associated with a certain bridge within a bridge stock. The three primary components of risk are considered: vulnerability, exposure, and hazard, which are specified according to the characteristics of the bridge, site, and road network. The bridge state of conservation is accounted for in the vulnerability component through average and critical defect condition indicators. The proposed method is implemented in a case-study of thirty real bridges in northern Italy. The impact of defects on vulnerability and priority ranking of bridges, the assumptions for defects condition and each subcomponent’s contribution to risk classification are discussed. The prioritization results are presented in absolute and normalized formats, with the latter providing a clearer view of the bridges’ relative priority within the stock. Finally, the proposed method’s findings are compared with the current Italian qualitative guidelines for bridge management.

Analysis of Arch Bridge Condition Data to Identify Network-Wide Controls and Trends

This paper outlines an initial analysis of 20 years of data held on an electronic bridge management database for approximately 3500 arch bridges across Northern Ireland (NI) by the Department for Infrastructure. Arch bridges represent the largest group of bridge types, making up nearly 56% of the total bridge stock in NI. This initial analysis aims to identify trends that might help inform maintenance decisions in the future. Consideration of the Bridge Condition Indicator (BCI) average value for the overall arch bridge stock indicates the potential for regional variations in the overall condition and the potential for human bias in inspections. The paper presents the most prevalent structural elements and associated defects recorded in the inspections of arch bridges. This indicated a link to scour and undermining for the worst-conditioned arch bridges. An Analysis of Variance (ANOVA) analysis identified function, number of spans, and deck width as significant factors during the various deterioration stages in a bridge’s lifecycle.

Use of Web-GIS Database Model to Accommodate Public Participation in the Improvement of Sustainable Road and Bridge Management: A Conceptual Idea

Sustainability is usually associated with energy source, ecological, soil and environmental aspects. Periodical road and bridge preservation is required to ensure the increasing of accessibility and mobility level, to increase regional economics’ growth, to minimize unplanned or unpredicted environmental consequences and to improve institutional arrangement capability as well. Road and bridge preservation is commonly managed based on its level of damage and constrained in budgeting. Poor public policy makes it worst. Therefore, the issue of strengthening the capacity of regional road and bridge management institutions is deemed important and urgent not only to reduce roads and bridges preservation cost but also to increase regional economics’ growth. Therefore, a proper road preservation type and priority should be determined based on technical, socio-economics, environment and public policy consideration. However, annual road and bridge functional condition data collection took high cost because it has been carried out by engineering consultant. The aim of this study is resolve it by providing a public participation portion in a road and bridge data base model. They are simply asked to send a real time condition of road s or bridge nearby to the local authority. The results strongly indicate that cost saved obtained from it could be focused to road and bridge preservation cost.

Bridge management systems: an overview and comparison

Bridges are one of the key elements of the transportation infrastructure of each country, and the reliability of the entire transportation network depends on their functioning. Maintaining bridges in proper technical condition is the main task of bridge management, for which more and more countries use bridge management systems (BMS). This study is devoted to comparing different BMS, reviewing the main functions and modules and determining the perspectives of development and implementation of the latest technologies in BMS. The analysed bridge management systems were compared by functions such as storage of bridge passport data, initial information on the bridge condition, assessment of the bridge condition, forecasting of the bridge condition and consideration of different maintenance strategies. Some systems are distinguished by the fact that they can predict future bridge condition, offer optimal maintenance strategies and take into account losses not only for the operation of structures, but also for transportation. Prospects for the development of BMS were also identified: the use of neural networks, the introduction of building information modelling (BIM) and the Internet of Things (IoT).

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.

A Data-Driven Model for Predictive Modeling of Vortex-Induced Vibrations of a Long-Span Bridge

Vortex-induced vibration (VIV) of long-span bridges can be of large amplitude, which can influence serviceability. Therefore, it is important to predict the response of vortex-induced vibration to aid the management of long-span bridges. A novel data-driven model is proposed to predict the time history of the dynamic response of VIV events. Specifically, the proposed model consists of gated recurrent unit (GRU) neural networks and the Newmark-beta method. GRU neural networks can perform accurate sequential prediction, and the Newmark-beta method can complement the physical meaning of the middle output of the proposed model. To aid the accurate prediction of the amplitude of VIV events, the proposed model employs weighted mean square error as the loss function, which can put more emphasis on the amplitude. The proposed model is validated on measured VIV events of a long-span suspension bridge. The weighted mean absolute percentage error and Pearson correlation coefficient of the trained model indicate the effectiveness of the proposed model.

Bridges damaged during the 2019 Poldokhtar flood: towards flood-resilient bridges

Bridges are among the most vital parts of infrastructure networks in every country, and have been seriously damaged by natural hazards such as floods and earthquakes in the past. Engineering communities have always faced challenges protecting these structures from flood hazards. Today, the world is facing climate change, with incidences of severe floods increasing globally. Flooding brings the possibility of significant bridge damage and therefore studying bridge performance under flood hazards is of vital importance. The flood in Poldokhtar, Iran, in 2019 was one of the most destructive floods in the last decade. In this research, the performance of 14 bridges during the Poldokhtar flood was reviewed and the damage incurred was assessed through field investigations. The aim of this paper is to demonstrate, based on visual inspections and engineering judgement, the different reasons and scenarios that lead to the destruction of different types of bridges during floods. The results of this study emphasise the need to estimate bridge vulnerability and develop procedures to prevent bridge destruction. The technical and statistical information on bridge destruction due to flooding obtained in this work is vital for the management and inspection systems of bridges, the retrofit of existing bridges and the design of new flood-resilient bridges.

Configuring Taiwan’s highway bridge performance indicator: a semi-quantitative risk assessment approach

The Taiwan Bridge Management System (TBMS) calculates the condition index (CI) of every highway bridge in its inventory using the latest findings from biennial routine inspections. However, the computational approach for CI can yield skewed results and make a bridge appear healthier than it actually is. To address this issue, a highway bridge performance indicator called the bridge risk index (RI) is presented. The RI is determined by combining the risks associated with the failure of each bridge component, which are evaluated using a semi-quantitative risk assessment approach. This evaluation considers the probability and consequence of a bridge component’s failure as a function of a component's deterioration state and criticality to the bridge’s overall structural safety and serviceability, respectively. To validate the effectiveness of the RI, inspection data from a sample of seven highway bridges were extracted from the TBMS and used to compare the RI with the CI. The results showed that the RI and its associated parameters can mitigate the inherent issues observed with the CI. Hence, the RI has the potential to be a valuable decision-support tool for transport agencies that rely on similar routine inspection processes to enhance their bridge management systems.

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.

State-Based Technical Condition Assessment and Prediction of Concrete Box Girder Bridges

The technical condition of bridges has become a crucial issue for organizing the maintenance and repairs in bridge management systems. It is of great practical engineering significance to construct an effective model for predicting the technical condition degradation of the bridge through the use of the historical inspection data. Based on the semi-Markov random process, this paper proposes a useful deterioration prediction model for bridges in the highway network. From the historical inspection data of the prefabricated concrete box girder bridges, the degradation curves of technical condition rating are obtained. The effect of bridge length on degradation rate of the prefabricated concrete box girder bridges is analyzed. According to the Weibull distribution parameters of different condition grades, the technical state degradation models for a bridge group and an individual bridge are proposed to predict the performance of the overall bridge and superstructure of the bridge. The results show that with the increase in bridge length, the degradation rate of bridge technical condition increases. The degradation rate of the technical condition of the superstructure is faster than that of the overall bridge. The proposed semi-Markov stochastic degradation model for the bridge group can not only predict the different condition ratings of the bridges at any time, but also predict the future deterioration trend of an individual bridge under any ratings.