Bridge Management Research Articles

Management of road bridge networks in Italy by means of integrated SHM systems

Management of stocks with large number of bridges is a relevant issue for the Italian roadway system. In the last years the use of integrated SHM (Structural Health Monitoring) systems is being introduced within the framework of the modern BMS (Bridge Management Systems) with the aim of implementing a continuous damage identification strategy. In this framework, ambient vibration-based SHM has received most attention owing to its minimal intrusiveness, non-destructive character and global damage identification capabilities. A lot of research effort has been devoted in the last years to SHM techniques but ease of use, interpretability and minimal intervention of expert judgement are essential issues for the fruitful application of SHM in real and wide bridge networks. ANAS S.p.A., the main road managing Authority in Italy, is implementing a huge monitoring program on its bridge network with the main purpose of addressing the recent indications of the Italian guidelines for assessing and monitoring of bridges, and the University of Padova, together with the University of Perugia, Politecnico of Milan, University of Pisa and the other Universities of FABRE Consortium are supporting ANAS for the real application of modern SHM techniques. According to these actual needs, a new software suite called P3P has been developed and is being applied to a huge stock of bridges distributed in all the Italian territory.

Modelos probabilísticos de previsão da deterioração de pontes baseados em matrizes de Markov, utilizando dados reais e simulados de modelos determinísticos

Abstract This study uses real and simulated information from 885 bridges in Brazil. A total of 2,655 available inspection data were collected from the database, and 37,170 additional data were simulated from deterministic deterioration prediction models developed in previous studies. The probabilistic Markov matrices-based models obtained include one covering all the bridges, specific models for non-aggressive and aggressive environments, and models for Average Daily Traffic (ADT) of less than and more than 4,000. Validation showed good metrics, with a coefficient of determination of 0.6268, a mean absolute error and mean squared error below 0.5, and an accuracy of 66.25%. Finally, these tools enable more accurate forecasting, and a better understanding of the risks associated with the deterioration of structures for safe and cost-effective bridge management.

Fatigue vulnerability assessment of a steel arch bridge using the probabilistic weibull model and considering its remaining service life under localized corrosion effects

This study attempts to obtain the approximate fatigue vulnerability of a steel arch bridge from the Colombian national road network. It establishes the interaction between the demand and the available capacity through the Weibull probability equation, and an AASHTO remaining-time equation indicated in (FHWA, 2015). However, the available capacity in terms of the S-N curve obtained from available international references was modified to account for the effects of corrosion present in elements and/or joints in the bridge. The load demand was modeled as the controlled action of the fatigue truck reported in AASHTO, which was systematically located on a structural model represented by Shell and Frame type finite elements, which represented the information available in drawings (in the current Colombian bridge management system), and the one obtained from structural survey activities. The structural model was further calibrated by a parametric study on the modulus of elasticity of steel, which included the minimization of the error difference in the vertical deflections of a bridge load test, keeping the magnitude within the expected ranges established in a protocol. Such reports of actual deflections were achieved using distance meters at important points of the bridge. This model allowed for estimating the magnitude of stresses in the structural elements of the bridge, especially in an area of interest for the study where chemical and physical elements of structural deficiency were determined. The results show an inverse relationship between the probability of failure and remaining service life. The effect of corrosion on the remaining capacity and the probability of failure of the structural elements is clear.

Analysis of the information flux model among all the actors involved in the use of a Bridge Management System according to Italian Guidelines: the BMS as a collaborative workplace

Bridges are critical elements in an infrastructural system for the resilience of the transportation network. It is important to guarantee proper maintenance of these structures since they are strategic for the economic and social welfare and support access to emergency services.The adoption of Italian Guidelines for the classification, risk management, safety and monitoring of existing bridges primarily introduces common instructions in periodic bridge inspections. At the same time, they suggest the use of digital instruments to collect data derived by the application of the guidelines.Bridge Management Systems (BMS) can absolve to this requirement and represent an additional value compared to a mere digital archive. From one hand, they are a tool for bridge owners to manage information to guarantee a proper maintenance while complying with budget and founding limitations defined by the Ministry. On the other hand, technicians can exploit them for data collection during bridge inspections and in-depth structural assessment.Thus, the creation of a collaborative workplace is one of the main requirements in the construction of a valid BMS. However, the information fluxes among the subjects can be complex inside a BMS from a management point of view. For instance, BMS must be a place where technicians can insert and receive information accordingly to their task and expertise. For this reason, it is important to ensure the possibility to differentiate data access levels and associate data property and responsibility.After briefly discussing the roles of each actor - technicians, bridge owners, Ministry - involved in the use of a BMS, this paper describes the interaction among all these figures and provides the flux model that has been implemented in INBEE, a new BMS that applies the latest version of the Italian Guidelines.

Predicting Load-Deflection of Composite Concrete Bridges Using Machine Learning Models

The main objective of this study is to predict accurately the load-deflection of composite concrete bridges using two popular machine learning (ML) models namely Random Tree (RT) and Artificial Neural Network (ANN). Data from 83 track loading tests conducted on various bridges in Vietnam were collected and analyzed. Various input parameters namely bridge's cross-sectional shape, length of concrete beam, number of years in use, height of the main girder, distance between the main girders were selected for the modelling. Validation indicators like R, RMSE, and MAE, and Taylor diagram were used for validation and comparison of the models. Results of this study showed that both RT and ANN are good for prediction of the load-deflection of composite concrete bridges, but RT outperforms ANN. Thus, the developed ML models can facilitate efficient bridge health monitoring and management by predicting the load-deflection of simple-span concrete bridges.

Application of Artificial Intelligence Technology in Bridge Construction and Maintenance

Artificial intelligence (AI) can extract valuable information from analyzing and learning among large amounts of data from bridge. In recent years, the researches and applications of AI in the field of bridge construction and maintenance are gradually increasing with the development of AI. This paper aims to summarize the application of AI in bridge construction and maintenance in recent years. Results show that AI technology can simulate and predict materials and construction scheme and promote the development of material science. In terms of intelligent detection, AI enables automated, efficient, and precise bridge inspection through technologies like visual machines and deep learning. Furthermore, in the realm of intelligent disaster prevention, early warning systems for potential bridge disasters can be achieved via big data analysis and predictive models. In the future, the design, construction and maintenance of Bridges will be increasingly combined with AI technologies, thus making the construction and management of Bridges more information-based and intelligent.

A GEO-DATABASE FOR 3D-AIDED MULTI-EPOCH DOCUMENTATION OF BRIDGE INSPECTIONS

Abstract. The recent collapse of bridges in Italy has prompted numerous studies on monitoring and maintenance. Many structures in Italy have been in service for over 50 years, necessitating new approaches to ensure their safety. To address this issue, Italy's Consiglio Superiore dei Lavori Pubblici (Superior Council of Public Works) has developed the Guidelines for Risk Classification and Management, proposing a multi-level approach to bridge management within a complex geomorphological environment. The guidelines outline a multi-level process that includes surveying the structures, conducting detailed inspections, and assigning risk classes based on hazard, exposure, and vulnerability. Current inspection processes are time-consuming and costly. Therefore, alternative monitoring technologies are crucial. Unmanned aerial vehicles equipped with cameras, laser technologies, and GPS systems offer flexible and cost-effective solutions for visual inspection. These technologies enable the collection of both quantitative and qualitative data, such as size, material properties, and overall condition. In this context, efficient data management and exploration systems are necessary to handle the vast amount of geo-referenced information. Multi-epoch databases play a crucial role in documenting the conditions of bridges and supporting a maintenance and structural health monitoring workflow. These databases can be utilized within a Bridge Management System to aid road managers in decision-making processes. Additionally, 3D exploration platforms provide visual analysis and highlight areas of interest within the structure. This work presents a multi-epoch geo-database that adheres to the Italian guidelines, offering optimized data management and queryability for 2D and 3D information. The entire process is designed using open-source and reproducible solutions.

Longitudinal vibration control strategy for long-span suspension bridges under operational and extreme excitations using eddy current dampers

A full-floating or a semi-floating system is conventionally employed in long-span suspension bridges (LSSBs) to reduce the excessive internal forces of main girders when subjected to various ambient loads/effects. However, such a system may induce appreciable back-and-forth longitudinal displacements of the main girder and result in the deterioration of auxiliary structural components (e.g., expansion joints). In light of this, this paper presents a vibration control strategy for mitigating the longitudinal girder vibrations of LSSBs under primary operational loads (i.e., wind and traffic) and extreme seismic effects using eddy current dampers (ECDs). A general computational framework of the seismic-wind-traffic-bridge system is first developed, whereby the longitudinal girder vibration characteristics of a prototype LSSB with and without ECD installations under various loading scenarios are investigated. Particularly, a novel multi-attribute decision-making (MADM) method is introduced to address a common challenge in developing optimal vibration control designs, i.e., the optimal vibration control design for one objective might adversely affect the others, which is especially critical from a life-cycle bridge management perspective. By using the MADM, typical bridge performance indices under various loading scenarios can be rationally involved in the associated optimization. The results show that different excitations can induce longitudinal girder vibrations with different dominant frequencies, which can be effectively mitigated by the ECD. However, for different bridge performance indices and different loading scenarios, the optimal ECD parameters may vary significantly. The proposed MADM method can help develop a balanced vibration control design that can ensure excellent bridge safety and serviceability simultaneously.

A new bridge management system based on spatial database and open source GIS

A new bridge management system based on spatial database and open source GIS

Real-time damage identification of hinge joints in multi-girder bridges using recursive least squares solution of the characteristic equation

Hinge joint damage is commonly observed in assembled multi-girder bridges, which may jeopardize their serviceability, safety, and sustainability. Timely detection of such damage is crucial for bridge management. Existing damage identification methods regarding hinge joints are either inconvenient in calculation or less sensitive to damage, and struggle with real-time monitoring. Therefore, to overcome these defects regarding assembled multi-girder bridges, this study proposes a real-time damage identification method that can directly estimate the stiffness of hinge joints without any complex finite element model. The proposed method is based on the recursive least squares solution of the characteristic equation of multiple beam systems, which is easy to implement and allows real-time evaluation. The effect of hinge joint damage on the dynamic characteristics of the bridge is quantified through numerical simulation, and the accuracy of the proposed method is verified. Finally, the proposed method is validated on an on-site measured bridge. The bridge consists of 16 girders with a length of 20 m. The results demonstrate that the proposed method practically achieves accurate identification of damage in the real bridge, indicating that the proposed method can be applied to the real-time monitoring of actual bridges to locate and assess hinge joint damage.

Identifying the Factors Impacting Bridge Deterioration in the Gulf Cooperation Council

The deterioration module (DM) is one of the four major modules necessary for any bridge management system (BMS). Environmental conditions, structural systems, bridge configuration, geographic location, and traffic data are some of the major factors that affect the development of deterioration modules. This emphasizes the need for the development of deterioration models that reflect the local conditions. In this article, some of the most important factors that could help in developing deterioration models in the Gulf Cooperation Council (GCC) were identified. The research was conducted in three phases; in the first phase, an extensive literature search was conducted to identify factors adopted in different deterioration models, and in phase two, the most relevant factors to the GCC environment were selected and these factors were further reduced based on input from local bridge experts. The result from the second phase is a list of factors identified by the experts. The identified list was utilized in phase three, which was focused on conducting a survey targeting bridge engineers to help identify the final selection and rank the factors according to their importance level. The results indicate that steel reinforcement protection, design load, chloride attack, type of defect, and age are the most important factors impacting bridge deterioration in the GCC. In addition, the time of rehabilitation; average daily truck traffic, ADTT; and average daily traffic, ADT, are the second most important factors. Factors with medium importance level are deck protection, services under the bridge, and inspection gap. The least important set of factors include temperature and wind load.

LSTM combined with BIM technology in the management of small and medium-sized span highway concrete beam bridges

To better carry out maintenance management for medium and small span highway concrete beam bridges, this bridge is taken as the research object. Building information modeling technology is applied to its maintenance management. The corresponding management system is constructed. Through this system, the distribution of diseases and other information can be viewed. Long short-term memory neural network is used to build the corresponding prediction model to predict the technical condition score of bridge components. The results show that the prediction error is small. The minimum error rate in component technical condition scoring prediction is 0.00 %. In the full bridge technology scoring prediction, the minimum scoring error is 2.8 points, and the maximum scoring error is 6 points. The minimum average error rate of this model in predicting component technical condition scores is 0.15 %. Except for individual components, the average error rate of all other components is less than 5 %. The accuracy of the prediction model is 95 %. The performance is superior to linear regression models. The average error rate in the abutment is 7.45 % lower than the linear regression model. This method can effectively predict the degradation of small and medium-sized span highway concrete beam bridges and achieve three-dimensional visualization of diseases.

Dynamic data‐driven railway bridge construction knowledge graph update method

AbstractEffectively integrating and correlating multisource data involved in the bridge construction process is crucial for the improvement of the bridge informatization level. In the current issues of dynamic numerous data and low information sharing between different engineering departments, the traditional information management methods are inefficient in providing comprehensive and accurate data support for construction safety. Focusing on the bridge construction stage, this article proposes a dynamic data‐driven construction method of railway bridge construction knowledge graph (KG) in combination with dynamic data (materials, personnel, equipment and sensors) in the construction process and KG technology. By taking a railway bridge as a case, the study develops a prototype system and analyzes the effectiveness of bridge construction KG in material traceability, personnel and equipment management and construction safety guidance, which can provide comprehensive and accurate data support for bridge construction management and construction optimization. The results show that: (1) bridge construction KG that takes into account the dynamic features of bridge projects can effectively integrate multiple elements; (2) the bridge construction KG is dynamically updated through real‐time comparison and advance prediction based on the dynamic data collected by multi‐sensing equipment at the construction site, and can provide effective data support for guiding bridge construction safety; and (3) the construction management prototype system based on railway bridge construction KG can provide accurate data support for material traceability, personnel and equipment management and assisted risk event decision‐making. The results of the comparative experiment between the KG group and the spreadsheet group showed that utilizing the KG saved approximately 50% of time and achieved a 20% higher accuracy rate in the material traceability task compared to the spreadsheet group. In general, this study proposes a dynamic data‐driven construction method of railway bridge construction KG, which can effectively realize the effective integration and management of multisource data in the bridge construction process, provide the necessary scientific basis for fine bridge management, and help to improve bridge informatization management level.

Endoscopic management of esophageal mucosal bridges in children with esophageal atresia

Background and study aimsEsophageal mucosal bridge (EMB) may be diagnosed at the anastomotic site in children operated on for esophageal atresia (EA) but so far only a few cases (n = 4) have been reported. This study aimed to characterize EMB in children with EA, risk factors, and treatment.Patients and methodsThis retrospective multicenter study recorded patient’s characteristics, EMB diagnosis circumstances, endoscopic management, follow-up, and EMB recurrence in children with EA aged less than 18 years, compared with paired EA patients without EMB.ResultsThirty patients were included (60% male, 90% EA/tracheoesophageal fistula, 43% associated malformations). Compared to 44 paired controls, EMB was associated with a history of nasogastric tube feeding (31% vs. 9.1%, p = 0.02) and severe gastroesophageal reflux disease (history of fundoplication: 41.4% vs. 13.6%, p < 0.01). 77% had symptoms (food impaction and/or dysphagia). Endoscopic management was performed in 53% of patients (83% electrocoagulation) with no technical difficulties or complications. 80% of the symptomatic patients with EMB improved after endoscopic treatment, independently of anastomotic stricture dilatation or not.ConclusionEMB endoscopic management by electrocoagulation is safe and often leads to symptom improvement.

Rotating Target Detection Method of Concrete Bridge Crack Based on YOLO v5

Crack detection is a critical and essential aspect of concrete bridge maintenance and management. Manual inspection often falls short in meeting the demands of large-scale crack detection in terms of cost, efficiency, accuracy, and data management. To address the challenges faced by existing generic object detection algorithms in achieving high accuracy or efficiency when detecting cracks with large aspect ratios, overlapping structures, and clear directional characteristics, this paper presents improvements to the YOLO v5 model. These enhancements include the introduction of angle regression variables, the definition of a new loss function, the integration of PSA-Neck and ECA-Layer attention mechanism modules into the network architecture, consideration of the contribution of each node’s features to the network, and the addition of skip connections within the same feature scale. This results in a novel crack image rotation object detection algorithm named “R-YOLO v5”. After training the R-YOLO v5 model for 300 iterations on a dataset comprising 1628 crack images, the model achieved an mAP@0.5 of 94.03% on the test set, which is significantly higher than other rotation object detection algorithms such as SASM, S2A Net, Re Det, as well as the horizontal-box YOLO v5 model. Furthermore, R-YOLO v5 demonstrates clear advantages in terms of model size (4.17 MB) and detection speed (0.01 s per image). These results demonstrate that the designed model effectively detects cracks in concrete bridges and exhibits robustness, minimal memory usage, making it suitable for real-time crack detection on small devices like smartphones or drones. Additionally, the rotation object detection improvement strategy discussed in this study holds potential applicability for enhancing other object detection algorithms.

Identifying bridges prone to instream wood accumulation: insights from bridges across the UK

Accumulation of instream large wood (i.e., fallen trees, trunks, branches, and roots) at bridges during floods may exacerbate flooding, scour and cause structural failure. Yet, explaining and predicting the likelihood of a bridge trapping wood remains challenging. Quantitative data regarding wood accumulation at bridges are scarce, and most equations proposed to estimate the accumulation probability were derived from laboratory experiments, and include variables such as flow velocity, Froude number, and approaching wood volume or size which are difficult to obtain. Other evaluations based on technical reports and information regarding wood removal have been proposed but are mostly qualitative. Until now, a data-driven approach combining multiple quantitative accessible variables at the river reach and catchment scales remains lacking. As a result, the controlling parameters explaining whether a bridge is prone to trap wood are still unclear. This work aims to fill this gap by analysing a database of 49 bridges across the United Kingdom (UK) classified as prone and not prone to wood accumulation. The database contained information regarding the geometry of the bridge (i.e., number of piers and pier shape) and we added parameters describing the upstream river channel morphology, the riparian landcover, and high-flow characteristics. We applied multivariate statistics and a machine learning approach to identify the variables that explained and predicted the predisposition of bridges to wood accumulation. Results showed that the number of bridge piers, the unit stream power, the pier shape, and the riparian forested area explained 87% of the total variability for the training dataset (0.87 training accuracy), and the selected model had a testing accuracy of 0.60 (60%). Although limited by the sample size, this study sheds light on the identification of bridges prone to wood accumulation and can inform bridge design and management to mitigate wood-related hazards.

Development of BrIM-Based Bridge Maintenance System for Existing Bridges

Globally, bridges are rapidly aging, and traditional maintenance approaches face significant challenges in terms of efficiency and cost. To overcome these challenges, considerable research has been conducted to introduce enhanced bridge management systems (BMSs) based on bridge information modeling (BrIM) from various perspectives. However, most studies have highlighted the advantages of BrIM, while neglecting the practical issues that potential users may encounter on existing bridges. The primary problem is digitizing existing bridges that have not yet adopted BrIM. The universal applicability of BrIM should be carefully considered from the perspective of national maintenance authorities managing thousands of bridges, because modeling based on commercial software is expected to be time-consuming and costly. Therefore, in this study, information and functional requirements were derived from interviews with stakeholders, including bridge owners, managers, and site inspectors. Based on this understanding, a data-driven modeling approach using basic bridge information was implemented, and an inventory code system was integrated to efficiently manage and utilize the data. Moreover, mapping and deep learning-based vectorization were considered for managing inspection information, and features for bridge assessment, dashboards, and reporting were incorporated to support decision-making. The developed BrIM demonstrated the potential for enhancing maintenance efficiency through a case study. Particularly, significant improvements were observed in mandatory documentation tasks, along with their investigation and analysis, as required by regulations. Additionally, efficient modeling and data management were achieved for the existing bridge.

Improvement of Socio-Economic Activities at the Island of Semau based on Sustainable Road and Bridge Management Strategy

A stable functional condition of roads is a guarantee of smooth, safe and comfortable socio-economic travel in an area. However, there is a limited ability to provide and/or manage development funds to preserve the road and bridge infrastructure. So far, every year the government tries to update the data base based on the result of road and bridge functional condition survey, intended to determine their priority handling. Result of field observation shows that the condition of some road links was in a poor level category so that it may affect the accessibility and mobility around the island of Semau, especially on the main connecting road to the location of main tourism objects. In addition, the type of land use and/or the availability of various supporting tourism facilities is very limited. These might influence the tourism area's attractiveness and/or the trip attractiveness due to the required time travel as well as travel cost. The limited or unavailability of local pavement material make it worst (high construction and/or preservation costs). Accordingly, the aim of this study is 1) to identify the determinant variables behind such transportation system problems, particularly based on the current physical and functional connectivity of roads and bridges, including variables that influencing the institutional capability arrangement 2) to recommend the appropriate strategy in road and bridge preservation and land use utilization. Keywords: Physical and functional connectivity; Surface distress index; Socio-economic trip; Sustainable management strategy; Transportation system management

Fast and Accurate Generation Method of Geometric Digital Twin Model of RC Bridge with Box Chambers Based on Terrestrial Laser Scanning

Digital Twin (DT) plays a crucial role in intelligent bridge management, and the geometric DT (gDT) serves as its foundation. Notably, the fast and high-precision generation of bridge gDT models has gained increasing attention. This research presents a method for generating high-precision and fast RC bridges with chambers for gDT using terrestrial laser scanning. The method begins with a proposed fast point cloud data collection technique designed specifically for bridges with internal chambers. Subsequently, Euclidean clustering and grid segmentation algorithms are developed to automatically extract contour features from the sliced point clouds. Finally, a framework based on the Dynamo–Revit reverse modelling method is introduced, enabling the automatic generation of gDT models from the identified point cloud features. To validate the feasibility and accuracy of the proposed method, a concrete variable section bridge is used. A comparison is made between the generated gDT model and the point cloud model in terms of 3D deviation, revealing a maximum deviation of 6.6 mm and an average deviation of 3 mm. These results affirm the feasibility of the proposed method.

Observation Maintenance for Bridges Using Early Detection of Deterioration Progress

In Japan, the results of inspections show that many bridges require repairs. However, Japanese municipalities do not have sufficient time and budget, and the progress of bridge repair is insufficient. Almost all the bridges managed by municipalities are short-span bridges. Therefore, a management system that selects and focuses on bridge repair based on performance evaluation would be effective. This paper presents an overview of a bridge management system that applies observational maintenance based on the early detection of deterioration, and its effectiveness is demonstrated.