Bridge Management System Research Articles

Recent Enhancement of Indian Bridge Management System

The Indian Bridge Management System (IBMS) was researched from 2003 and has finally been implemented since 2015. During the four years implementation phase of the system, many shortcomings were encountered and lessons learned. This enabled research to continue and to enhance IBMS to create the Unified Bridge Management System (UBMS). The article enumerates various developments in IBMS that resulted in the evolution of UBMS. The article further narrates the integration of innovative technologies in UBMS that have resulted in the elimination of most of the shortcoming that were identified and noticed during implementation. The key function of any bridge management system is the management of funds during the entire lifecycle of the bridges, which depends on the deterioration model and the assessment of risk. Inventory gathering, inspections and testing are all major activities that fall under the remit of a bridge information system and are essential for the maturation of bridge management. One of the focuses driving research the world over has been to make deterioration modelling more realistic. Deterioration modelling, which to date has been based on the judgement of the engineers undertaking inspections, has moved to accommodate factual data based on the performance monitoring of the distressed elements of bridges. The Bridge Information Model (BIM) evolved as a solution and tool for gathering information about bridge design and the construction phase. Management of the lifecycle is now being explored within BIM. The integration of BIM with BMS has ensured the maximization of the utilization of the strengths of both BIM and UBMS. This integration has resulted in the enhanced efficiency of predictive tools essential for bridge management.

Deterioration Model for Reinforced Concrete Bridge Girders Based on Survival Analysis

The prediction of bridge service performance is essential for bridge maintenance, operation, and decision making. As a key component of the superstructure, the performance of the main girders is critical to the structural safety of the bridge. This study makes full use of the inspection records from the Bridge Management System (BMS) in Shanghai and performs pre-processing work on a large amount of data. Recent advances in survival analysis were utilized to investigate the inspection records of over 40,000 reinforced concrete bridge main girders over a 14-year period. Survival analysis methods based on the Weibull distribution were used to predict the service performance of the main girders, and, in addition, a COX proportional hazards model was used to analyze the effect of different covariates on the survival of the main girders. The results show that the deterioration rate of main girders increases with age, with an average life of 87 years for main girders in Shanghai. The grade of the road on which the bridge is located and the position of the main girder in the bridge superstructure have a significant impact on the probability of survival of the main girder. It can be concluded that more attention should be paid to the inspection and maintenance of side girders on branch roads to reduce the pressure on bridge management in the future. Furthermore, the analysis in this study found that the deterioration rate of the main girders is faster than the deterioration rate of the whole bridge and superstructure, and, therefore, more attention and necessary preventive maintenance measures should be taken in the maintenance and management of the main girders.

Development of a schema for the remote inspection of bridges

Visual inspection remains key for assessing the condition of bridges and hence assisting with planning and maintenance activities. There have been many efforts to improve or supplement visual inspection processes using new sensing technologies and data capture methods to usher in an era of ‘smart bridges’ or ‘smart infrastructure’. One method to improve data capture is a ‘remote inspection’ where inspectors use digital photographs of a bridge to identify and grade structural defects to the standard of a ‘general inspection’. In this paper, survey data are presented to help formulate a preliminary assessment of the potential for engineers to implement this possible evolution of the visual inspection process. A potential schema for remote visual inspections is developed and presented as a conceptual web application. The focus on the development of the schema includes the need for ease of use by inspectors and integration of collected digital data into bridge management systems. The suggested platform is seen as a transitional method to aid in the long-term implementation of further automation of the inspection process. The system architecture is provided along with possible technologies that may support or enhance it, as well as a discussion of the potential barriers to implementation.

Railway Bridge Management System Based on Visual Inspections with Semi-Markov Continuous Time Process

This article describes a model for predicting the degradation of in-service railway bridges based on a semi-Markov continuous time process. This model relies on the history of inspections of 588 bridges located on a heavy-haul railway line in Brazil, between 2016 and 2020. A dedicated computational tool developed in Matlab allows the automated data processing. A parametric study is performed to understand which factors derived from the bridge structural characteristics, as well as operational and environmental factors, most influence the deterioration model. The type of material proves to be a decisive factor and therefore two specific prediction models are stablished, one for concrete bridges and other to steel bridges. The prediction models have an efficiency equal to 93.7%, for concrete bridges, and 95.1% for steel bridges. Additionally, the analysis of several preventive and corrective maintenance scenarios, specifically for concrete bridges, allows to optimize the condition ratings during the life cycle.

An Enhanced Information Retrieval Method Based on Ontology for Bridge Inspection

Bridge management systems (BMSs) are widely used to assist an inspector in performing element-level bridge inspection. Retrieving and determining target elements to be inspected becomes an important factor in the efficiency of bridge inspection. This paper presents an enhanced information retrieval (IR) method based on ontology to predict the target elements. The novelty of this method is that an improved seven-step method based on automatic mapping technology is proposed to construct a new bridge inspection ontology (BIontology), which provides a knowledge base for the present IR method. A further novelty is that a new software architecture is designed for integrating ontology, and a promising prototype system based on the software architecture is developed to realize the present IR method using SPARQL query. In addition, a novel prediction algorithm based on the present IR method is proposed to automatically recommend the target elements. A case study of ontology construction is performed to demonstrate that the improved seven-step method can accelerate the construction of the BIontology compared with the manual method. A case study of bridge inspection is implemented to verify that the proposed algorithm outperforms an existing method, thereby validating the effectiveness of the present IR method.

ДЕФЕКТИ МОНОЛІТНИХ ТРАНСПОРТНИХ СПОРУД І МОЖЛИВІ ШЛЯХИ ЇХ УСУНЕННЯ

Introduction. Defects that occur during the operation of monolithic transport structures, in particular overpasses, rectangular pipes and cattle drives, are considered. A comprehensive approach to the selection of constructive solutions, materials and technologies is proposed for their prevention and improvement of some provisions of the current norms for the design of such structures. Problem Statement. In the constructions of span structures of monolithic rectangular pipes, overpasses and cattle runs, during their operation under load, transverse cracks of the span structure appear, as well as shrinkage cracks, which can harm their normal operation. Purpose. The purpose of the work is the need to draw the attention of balance-keepers of structures, work customers and project organizations to the importance of ensuring proper indicators of the operational condition of transport structures at all stages of the life cycle, from project development and construction to the performance of regular work on their operational maintenance. Materials and methods. Research methods consist in identifying the influence of structural defects of monolithic transport structures on durability and safe operation. Analytical materials were used, in particular, based on the results of supervision of monolithic transport structures during the implementation of scientific support for their construction or reconstruction, as well as based on the results of survey and diagnostic work during the updating and filling of the analytical expert bridge management system (AESUM) at the facilities Poltava region. Results. The paper analyzes and provides recommendations for preventing cracking and eliminating defects in the constructions of monolithic transport structures. Conclusions. Monolithic transport facilities require complex solutions for the design of span structures and coverings, ensuring the proper organization of water drainage and compliance with the requirements of current regulatory documents and technological regulations during construction and operational works. When performing calculations of monolithic span structures of transport facilities, stricter requirements should be put forward regarding crack resistance indicators when calculating the formation and opening of cracks. It is also necessary to pay more attention to the technology of installing a cement-concrete coating. In order to reliably establish the bearing capacity of monolithic span structures, it is proposed to investigate dynamic effects during the inspection of structures and take into account the actual forces from moving loads in the presence of irregularities formed on the carriageway.

АНАЛІТИЧНА ІНФОРМАЦІЯ З ПРОГРАМНОГО КОМПЛЕКСУ АЕСУМ ЩОДО МОСТІВ СТАНОМ НА 2022 РІК

Introduction. Effective development and maintenance of transport infrastructure is the key to the strategic development of the state. Bridges are the objects of critical infrastructure and, accordingly, an important component of it. In order to make effective management decisions at the level of the State Road Agency of Ukraine in the field of bridge construction, an Analytical Expert Bridge Management System (AESUM) software complex is implemented. Problem statement. Actually, AESUM contains brief technical information on all bridges on public roads, namely on 16,142 transport facilities. Bridge economy is a living system where everything is changing : the number of bridges due to the construction of new ones, the transformation of small bridges into culverts; operating organizations, and also, which is very important — the technical state of bridges. Today, the sampling of bridges inspected and included in the AESUM software complex is 75 % on roads of state importance, 15% on local roads. Accordingly, information on bridges is constantly changing and needs to be updated. It is also important to improve the bridge operation strategy considering the scientific and analytical apparatus of AESUM. Objective. Implementation of the effective strategy for bridges operation on the roads in order for planning financial resources for their repairing and maintenance. Providing the general public with up-to-date statistical information on bridges from AESUM will be useful for use in scientific and scientific-practical purposes, and the identification of certain analytical trends will allow improving AESUM regarding the formation of a bridge operation strategy. Materials and methods. Analytical, statistical, system analysis. Results. The study presents analytical and statistical information about bridges on the public roads of Ukraine. Certain trends and keen attention to the technical condition of bridges have been observed. Conclusions. Actually The technical state of road bridges in Ukraine at the moment is unsatisfactory, which causes significant social and material damage and requires an increase in allocations for their repair and reconstruction. Currently, there is an urgent need for improving and implementing strategies for bridges operation, considering the scientific and analytical apparatus of AESUM for optimal priority planning and timely implementation of rehabilitation works on bridges and maintenance of bridges in proper state with rational use of financial resources.

ASSESMENT KONDISI JEMBATAN RANGKA BAJA CALLENDER HAMILTON DENGAN MENGGUNAKAN METODE BRIDGE MANAGEMENT SYSTEM

The Callender Hamilton (CH) bridge type is one of the oldest bridges that are still widely used to connect city roads, especially in Aceh, where the volume of vehicles that grows up every year is an alert for the public. Accordingly, a bridge management system is essential to handling and planning an assessment condition value of the bridge. The purpose of this study was to evaluate and determine the proper management of the CH bridge in North Aceh and East Aceh by using the Bridge Management System (BMS) method. This research was conducted on 4 bridges, namely Krueng Pase Geudong, Alue Asam Kumbang, Krueng Peudawa Rayeuk, and Krueng Sei Raya. Firstly, the results obtained on the condition value of the Krueng Pase Geudong, built-in 1995, is 3 (high damaged) which proposed rehabilitation treatment and its equivalent age is 42 years. The result is poor because the equivalent age of the bridge was higher than the age of the bridge. Secondly, on the Alue Asam Kumbang, built-in 1977, the value of the condition obtained is 3 (high damaged) which proposed rehabilitation treatment and the equivalent age is 42 years. This proves that the maintenance was quite good because the equivalent age of the bridge was smaller than the age of the bridge. Thirdly, on the Krueng Peudawa Rayeuk, built-in 1979, the value of the condition obtained was 2 (low damage) which proposed handling of routine/periodic maintenance, and the equivalent age is 32 years. This proves that the maintenance was quite good because the equivalent age of the bridge was smaller than the age of the bridge. The last one is the Krueng Sei Raya that the condition value built-in 1994 was 2 (low damage) which proposed routine/ periodic maintenance and the equivalent age is 32 years, this proves that the maintenance was poor since the equivalent age of the bridge is higher than the age of the bridge.

A Scoping Review of Information-Modeling Development in Bridge Management Systems

Transportation assets represent a critical element of public infrastructures, and bridge networks are an essential part of these assets. A system that includes several tools for managing the bridges is called a bridge management system (BMS). BMSs are built up to provide long-term health verification for bridges and assure safe serviceability. With the advent of powerful computers in the mid-1980s, BMSs became more electronic and, consequently, capable of processing more data than a paper system. Building information modeling (BIM) is an emerging technology with commanding visualization and informatization ability, making it a perfect tool for generating modern management systems. Bridge information modeling (BrIM) is a developed concept extracted from BIM that concentrates on bridges. In this paper, the use of BrIM in the development of bridge management systems during the recent decade is verified through a scoping review of the state-of-the-art literature. The broad nature of the scoping review provides chances for rapidly reviewing evidence in emerging topics and analyzing knowledge gaps; it is used to capture the cutting-edge concept of information modeling in the body of literature. In this study, search resulted in a final refined list of 78 journal articles published in the recent decade, categorized into six different topic areas. This paper aims to verify different aspects of using information modeling in bridge management and identify the knowledge gaps, limitations, and emergent works for industrial and academic investigators. Descriptive and content analyses are presented to clarify the researchers’ focal points in this review, which shows trending keywords, leading journals, and articles’ frequency in each field. This study indicates that the concentration of BrIM researchers is on new inspection and test methods and maintenance optimization with a particular focus on concrete structures.

On the load-carrying capacity produced by different load models for road bridges

The evaluation of existing bridges is an important process and source of information within the bridge management system. Except for the load-carrying capacity represents the basic evaluation parameter, it moreover expresses a relevant and decisive parameter for the transition of actual road traffic. The paper presents the continuation of an already published analytical study aimed at comparing the effects of traffic load produced by different load models on road bridges. There are presented calculated load-carrying capacities for model cases of bridges with an upper deck with eight alternative spans and six different cross-sectional arrangements. On each of them, multiple load models simulating the effects of traffic were considered, while a certain predefined part of the considered load represented the required weight of the vehicle defining the load-carrying capacity of the respective bridge. The discussion is mainly focused on the pitfalls and possibilities of correctly defining the dependence of the weight of the vehicle representing the load-carrying capacity from the traffic load consideration schemes themselves, which are mostly defined by design codes.

Deep Reinforcement Learning–Enabled Bridge Management Considering Asset and Network Risks

Bridges deteriorate over time due to various environmental and mechanical stressors. Deterioration is a significant risk to bridge owners (asset risk) and the traveling public (network risk). To tackle this issue, transportation agencies carry out bridge management under limited resources to preserve bridge conditions and control the risks of bridge failure. Nonetheless, existing network-level analysis for bridge management cannot explicitly consider the effects of preservation actions on network risk, measured directly by functionality indicators such as network capacity. In this paper, a novel method based on deep reinforcement learning is proposed to devise network-level preservation policies that can reflect bridge importance to network functionality. The proposed method is based on the proximal policy optimization algorithm adapted for bridge management problems and improved via distributed computing and architecture. The method is applied to an illustrative bridge network. The results indicate that the proposed method can produce significantly better preservation policies in terms of minimizing long-term costs that include asset and network risks. The devised policies are also investigated in depth to allow for transparent interpretation and easy integration with existing bridge management systems.

Developing Bridge Deterioration Models Using an Artificial Neural Network

The condition of a bridge is critical in quality evaluations and justifying the significant costs incurred by maintaining and repairing bridge infrastructures. Using bridge management systems, the department of transportation in the United States is currently supervising the construction and renovations of thousands of bridges. The inability to obtain funding for the current infrastructures, such that they comply with the requirements identified as part of maintenance, repair, and rehabilitation (MR&R), makes such bridge management systems critical. Bridge management systems facilitate decision making about handling bridge deterioration using an efficient model that accurately predicts bridge condition ratings. The accuracy of this model can facilitate MR&R planning and is used to confirm funds allocated to repair and maintain the bridge network management system. In this study, an artificial neural network (ANN) model is developed to improve the bridge management system (BMS) by improving the prediction accuracy of the deterioration of bridge decks, superstructures, and substructures. A large dataset of historical bridge condition assessment data was used to train and test the proposed ANN models for the deck, superstructure, and substructure components, and the accuracy of these models was 90%, 90%, and 89% on the testing set, respectively.

An Intelligent Bridge Management and Maintenance Model Using BIM Technology

In recent years, the bridge construction industry of China has developed rapidly. With the increase in bridge service lifetime, numerous damages and cracks have appeared that pose a direct threat to its structure and transportation safety. Therefore, a new approach to bridge management and maintenance is proposed using building information modeling (BIM) system. The objective of this paper is to monitor and manage bridge problems in real time. It will develop an intelligent bridge management and maintenance system model. This system will improve bridge management and maintenance and reduce the number of accidents on unsafe bridges. According to the data, the management and maintenance units can adopt scientific and reasonable methods to manage and restore bridges. This will ensure the safety and property of people. To improve the efficiency of bridge management and maintenance, a visible and accurate model is built with a combination of BIM technology. In this model, bridge damages are controlled and analyzed using BIM technology. The results show that the changes in the cracks and damage to the bridge within a year continue to rise from January to September and decrease from September to October. The crack of width and length continues to increase from October to December. As a case study, Tianyuan Bridge is selected to evaluate the technical condition of the bridge. The overall evaluation of the bridge result indicates a class II bridge.

Asset Management of Existing Concrete Bridges Using Digital Twins and BIM: a State-of-the-Art Literature Review

Abstract The need to optimize investments in bridge maintenance has created a demand for improved bridge management systems (BMS). Outdated practices in bridge inspection and constant advances in information technology have also contributed to this demand. The use of Digital Twins (DT), although well established in other industries, is still incipient for asset management and structural analysis of bridges. There is a great deal of research on Building Information Modelling (BIM) for bridge inspection, but its post-construction potential is still under-explored. This study presents a state-of-the-art review of the literature on asset management for bridges using digital models such as BIM and digital twins. The review was conducting using a systematic approach. Despite the rapid increase in research on DT and the amount of existing research on BIM, several gaps remain to be addressed, such as the lack of consensus about the definition of digital twins, which has led to wrongful categorisation of digital models as DT. The complex data flow and software compatibility required to develop a functional DT have hindered the exploitation of their full potential so far. The integration of BIM post-construction to BMS and existing automation technologies can also significantly improve current practices of bridge management.

ANALISIS DAN STRATEGI MANAJEMEN JEMBATAN PADA SEGMEN JALAN NASIONAL YETTY-UBRUB-YAMBRA-TOWE HITAM

The Yetty-Ubrub-Yambra-Towe Hitam section is one of the important sections in the Papua region because it is the main route to Jayapura which is the capital of Papua Province and this section is included in the national road section. Beside the developing of road pavements, this segment also needs to be supported by the construction and maintenance of bridges. At some points, adequate bridges have been installed. However, at some points it is not sufficient, even the bridge has been badly damaged and the width is only sufficient for one vehicle. This study aims to analyze the bridge management system that has been carried out and then develop a better bridge management strategy for this section. The research location covers the length of the segment and the analytical method used is qualitative analysis. The results indicate that so far the bridge management has not been implemented properly in this section. Therefore, it is necessary to improve the bridge management system that has been carried out. Some things that need to be improved are strategies including implementation time, inspection process, audit staff, reporting, recording of each bridge and filing of reports.

Life-Cycle Assessment of Deteriorating RC Bridges Using Artificial Neural Networks

Life-cycle structural assessment of existing bridges under aging and deterioration processes is of paramount importance for authorities managing road networks. In fact, in most developed countries many bridges and infrastructure facilities are approaching 50 years of lifetime or are even older, and a significant percentage of them are prone to be rated as structurally deficient. In this context, a quick and reliable estimation of the bridge condition is needed for the prioritization of maintenance and repair interventions and the optimal allocation of resources over large bridge stocks. In the last decades, computerized bridge management systems (BMSs) emerged worldwide to support decision makers in the definition of operational management policies. BMSs often incorporate soft computing tools, such as artificial neural networks (ANNs), which can provide reliable bridge assessments over time under uncertainty on the basis of limited data stored in bridge databases. This paper proposes an approach to the life-cycle assessment of deteriorating reinforced concrete (RC) bridges based on ANNs. Two-layer ANNs are formulated and trained for this purpose. The proposed approach is preliminarily applied to a time-variant structural capacity assessment of a RC bridge deck cross-section under chloride-induced corrosion to compare the results obtained with both full and limited input datasets. Finally, the methodology is applied to the prediction over time of the condition state of a group of existing RC bridges based on the information stored in the US National Bridge Inventory.

Sustainability-oriented maintenance management of highway bridge networks based on Q-learning

Current bridge management systems provide maintenance strategies that balance costs and structural safety of bridge networks. However, they are cost-oriented rather than sustainability-oriented. Moreover, the existing literature lacks research on reducing the environmental impacts of a bridge network during operation and maintenance periods. This paper develops a condition-based maintenance approach for highway bridges, aiming to minimize the total carbon emissions of bridge networks subject to maintenance budget constraints. A two-dimensional Markov chain model is applied to predict the deterioration processes of bridges, and a Q-learning algorithm is proposed to determine the maintenance strategy for a single bridge. Then, integer programming optimizes the budget allocation process for a bridge network. The proposed method is demonstrated using the concrete bridges in New York state. Sensitivity analyses indicate the impacts of budget levels on maintenance plans and total carbon emissions. 10% drop in budget level results in 30 additional bridge repairs delayed as well as 39 tonne extra annual carbon emissions. The proposed optimization framework would contribute to intelligent infrastructure asset management and sustainable society.

INTEGRATION OF UAV-LIDAR AND UAV-PHOTOGRAMMETRY FOR INFRASTRUCTURE MONITORING AND BRIDGE ASSESSMENT

Abstract. The health assessment of strategic infrastructures and bridges represents a critical variable for planning appropriate maintenance operations. The high costs and complexity of traditional periodical monitoring with elevating platforms have driven the search for more efficient and flexible methods. Indeed, recent years have seen the growing diffusion and adoption of non-invasive approaches consisting in the use of Unmanned Aerial Vehicles (UAVs) for applications that range from visual inspection with optical sensors to LiDAR technologies for rapid mapping of the territory. This study defines two different methodologies for bridge inspection. A first approach involving the integration of traditional topographic and GNSS techniques with TLS and photogrammetry with cameras mounted on UAV was compared with a UAV-LiDAR method based on the use of a DJI Matrice 300 equipped with a LiDAR DJI Zenmuse L1 sensor for a manual flight and an automatic one. While the first workflow resulted in a centimetric accurate but time-consuming model, the UAV-LiDAR resulting point cloud’s georeferencing accuracy resulted to be less accurate in the case of the manual flight under the bridge for GNSS signal obstruction. However, a photogrammetric model reconstruction phase made with Ground Control Points and photos taken by the L1-embedded camera improved the overall accuracy of the workflow, that could be employed for flexible low-cost mapping of bridges when medium level accuracy (5–10 cm) is accepted. In conclusion, a solution for integrating interactively final 3D products in a Bridge Management System environment is presented.

Path Planning and Control of a UAV Fleet in Bridge Management Systems

Traditional methodologies for precise inspection of bridges (pavement, beams, column cap, column, joints and inside box girder, etc.) with By-bridge equipment, Aerial Work Platform (AWP) or via ropes have several limits that can be overcome by using Unmanned Aerial Vehicles (UAVs). The constant development in this field allows us to go beyond the manual control and the use of a single UAV. In the context of inspection rules, this research provides new inputs to the multilevel approach used today and to the methods of structural inspection with drones. Today, UAV-based inspections are limited by manual and/or semi-automatic control with many restrictions on trajectory settings, especially for areas of difficult access with Global Navigation Satellite Systems (GNSS) denied that still require the intervention of a human operator. This work proposes the use of autonomous navigation with a fleet of UAVs for infrastructural inspections. Starting from a digital twin, a solution is provided to problems such as the definition of a set of reference trajectories and the design of a position controller. A workflow to integrate a generic Bridge Management System (BMS) with this type of approach is provided.

Integration of AASHTO Element Data Into the New York State Department of Transportation Bridge Management System: Treatments and Project Selection

The New York State Department of Transportation (NYSDOT) started collecting AASHTO element-based inspection data as part of bridge inspections in 2016 after retiring its legacy condition inspection system that was in use for more than four decades. A research project was initiated to develop a methodology to integrate AASHTO element-based inspection data into NYSDOT bridge management decision-making processes, guidelines, and bridge performance measures. A logic was developed, based primarily on AASHTO element inspection data, for identifying the work needs for each bridge and predicting the change in element condition states should the resulting work recommendations be implemented. Based on a thorough evaluation of the developed logic, appropriate changes to NYSDOT’s bridge management system are recommended. This paper briefly describes the current study and findings.