Bridge Maintenance System Research Articles

Self‐Powered Intelligent Damper Integrated Triboelectric‐Electromagnetic Hybrid Unit for Vibration In Situ Monitoring of Stay Cables

AbstractMonitoring and controlling the vibration of bridge cables can maintain the health of the bridge structure and prolong the service life. This work proposes a novel self‐powered intelligent damper (SID) integrated triboelectric‐electromagnetic hybrid unit for vibration in situ monitoring of stay cables. Among them, the sensing function of the SID is realized by an arched membrane triboelectric nanogenerator (AM‐TENG), and the energy harvesting is realized by a three‐phase electromagnetic generator (TP‐EMG). The working principle and performance are investigated. Rotating and linear experimental platforms are built to verify the SID's low‐frequency (0.25–1.50 Hz) performance, including sensing, power generation, and vibration reduction. The experimental result indicates that the fitting vibration speed coefficient of the bridge cable with the AM‐TENG signal frequency is 0.990. In addition, the TP‐EMG achieves a peak power of 1.21 W. At 0.1 A power supply, the SID can output a damping force of 50 N. Finally, an experimental system is constructed to verify the viability of the SID in bridge cable vibration monitoring and vibration reduction. This work provides a feasible solution for further optimizing the bridge structure health monitoring and maintenance system.

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.

Development of Data-based Hierarchical Learning Model for Predicting Condition Rating of Bridge Members over Time

Bridge maintenance strategy is implemented to ensure that effective decisions are made based on the evaluation of the current performance and predicted future conditions of a bridge. The current bridge maintenance system relies on traditional manpower-based methods to determine bridge performance. However, the present techniques employ a material deterioration model that has uncertainty in predicting bridge performance. Moreover, the utilization of collected bridge inspection-related data is insignificant, and related research is lacking. Accordingly, a new type of bridge deterioration model is proposed using state performance data based on bridge inspection. To formulate the deterioration model, bridge condition inspection data accumulated over a long period were utilized. The data have been continuously updated since 1992 by the US Federal Highway Administration and consist of basic information and various influencing factors of bridges. The developed model uses the long short-term memory (LSTM) algorithm (a type of recurrent neural network) as well as layer normalization and label smoothing to improve the applicability of fundamental data. For the stable application of data, predicting the model performance for up to 30 years every 2 years is possible. By implementing the many-to-many type of the LSTM algorithm, the predicted probability of each grade derived for each sequence was weighted and averaged with the grade value to derive a continuous state grade result. Thus, the proposed model can express discrete historical degradation indices in continuous form according to the service life. In addition, it enables the prediction of bridge performance using only the fundamental information regarding new and existing bridges. For the effective use of basic data, an optimization model was derived using preprocessing and various regularization techniques. Additionally, a feed-forward deep learning model and stochastic model were developed using the same data. For performance assessment, a regression analysis evaluation method was applied, and comparative analysis was performed using the inspection data of an actual bridge. The use of a time-series model enabled the continuous and stable prediction of bridge performance.

A Reliability-Based Framework for Damage Accumulation Due to Multiple Earthquakes: A Case Study on Bridges

Damage accumulation due to multiple seismic impacts over time has a significant effect on the residual service life of the bridge. A reliability-based framework was developed to make decisions in bridge maintenance activities. The feature of the framework enables quantifying the time-dependent probability of failure of bridges due to the impact of multiple earthquakes and progressive deterioration. To estimate the reliability of the bridge systems, the probability of failure of the bridge was used. Two case studies were utilised to demonstrate how the method can be applied to the real world. Results show that the accumulated damage caused by multiple earthquakes and progressive deterioration significantly impact the remaining useful life of the bridge. Furthermore, the soil conditions predominantly influence the progressive deterioration and reduce the service life of the bridge. Overall, the proposed framework enables the sustainable decision-making process for bridge maintenance activities. The results reveal the necessity of including the combined impact in the bridge maintenance system and that there is a more than 40% increase in the probability of failure, due to the combined effect of progressive deterioration and earthquake impacts, compared to the impact only due to seismic loads for the considered case study bridge.

Model of Predicting the Rating of Bridge Conditions in Indonesia with Regression and K-Fold Cross Validation

Maintenance and repair of the bridge are inevitable in the operation of a bridge to maintain its condition to keep the operation. Indonesia has hundreds of thousands of bridges that are still actively in use. The classic problem with infrastructure management, such as bridges, is that large numbers are generally not balanced with adequate bridge maintenance budgets. Therefore,the strategy of implementing maintenance and repair by preparing priorities becomes the only logical approach. To get a priority scale, a scoring mechanism is needed. The assessment used by the Ministry of Public Works and Public Housing (PUPR) especiallythe Bina Marga field is based on the bridge management and maintenance system, namely Bridge Management System (BMS) 1993. With BMS 1993, the condition of the bridge is represented by the Condition Value (NK) of the bridge. This study is based on existingNK, prediction of NK value in the future. The predicted model developed is with regression models. Regression models are combined with k-fold cross-validation to improve the accuracy rate of the model. The developed model produces regression models for all variables of condition values with a low error percentage that is in the range of MAPE = 10% and RMSE 0.15. Further significance tests with ANOVA are also conducted to test the effect of independent variables on dependent variables, including testing on fit models to show the resulting model does not overfit and/or underfitting.

Development of a BIM-based bridge maintenance system (BMS) for managing defect data

Bridges might experience many defects during use, such as pavement cracks and reinforcement corrosion, which easily produce an accumulated impact that threatens bridge safety. Thus, there is a need for the regular inspection and maintenance of bridges. This paper presents a bridge maintenance system (BMS) based on building information modelling (BIM), which is utilized in bridge defect information management using a digitalization method. A bridge defect three-dimensional BIM (BIM3D) library is established and combined with a bridge model to visualize bridge defect conditions. Based on bridge inspection data, bridge defect information is digitally classified and encoded according to the international framework for dictionaries (IFD) standard and used to establish a database. An evaluation of bridge technical conditions is performed, and the results are graded and displayed in different colours, reflecting the visualization function of BIM technology. Maintenance suggestions are provided according to the range of bridge technical condition scores, reflecting the informatization function of BIM technology. With the Xinjiang Cocodala Bridge in China as a case study, a bridge BIM3D model and inspection data are imported into the BMS to utilize the functions of ‘visualization of bridge defect conditions’, ‘evaluation of bridge technical conditions’ and ‘recommendations of bridge maintenance methods'.

A Decision-Making Algorithm for Concrete-Filled Steel Tubular Arch Bridge Maintenance Based on Structural Health Monitoring.

This study focuses on establishing a novel heuristic algorithm for life-cycle performance evaluation. Special attention is given to decision-making algorithms for concrete-filled steel tubular (CFST) arch bridge maintenance. The main procedure is developed, including the ultimate loading-capacity modeling of CFST members, multi-parameter selection, ultimate thresholds presetting based on the finite element method, data processing, crucial parameters determination among sub-parameters, multi-parameter regression, ultimate state prediction, and system maintenance decision-making suggestions based on the multi-parameter performance evaluation. A degenerated ultimate loading-capacity model of CFST members is adopted in the finite element analysis and multi-parameter performance assessment. The multi-source heterogeneous data processing and temperature-effect elimination are performed for the data processing. The key sub-parameters were determined by the Principal Component Analysis method and the Entropy-weight method. The polynomial mathematical model is used in the multi-parameter regression, and the ±95% confidence bounds were verified. The system maintenance decision-making model combines the relative monitoring state, the relative ultimate state by the numerical analysis, and the relative residual life of degenerated members. The optimal system maintenance decision-making suggestions for the bridge maintenance system can be identified, including the most unfavorable maintenance time and parameter index. A case study on a CFST truss-arch bridge is conducted to the proposed algorithms. The obtained results demonstrated that the crack width deserves special attention in concrete bridge maintenance. Additionally, these technologies have enormous potential for the life-cycle performance assessment of the structural health monitoring system for existing concrete bridge structures.

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.

Development of a Bridge Maintenance System Using Bridge Information Modeling

Bridges play a critical role in the transportation system network; accordingly, assuring satisfaction with the service level of these structures is vital for bridge maintenance managers. Thus, it is vital to determine the optimum bridge maintenance plan (i.e., the optimum timing and type of repair activities applied to the bridge elements) considering the budget limitations. To optimize the bridge maintenance plan, some researchers have focused on developing optimization models, including the Genetic Algorithm (GA). However, a few studies have employed Bridge Information Modeling (BrIM) to enhance bridge maintenance management. This study focuses on developing an integrated framework based on BrIM and bridge maintenance optimization to utilize visualization capabilities of BrIM to assist maintenance managers in making decisions. The presented framework optimizes the bridge maintenance plan at the sub-element level. The BrIM automatically feeds into the developed GA optimization system. The introduced framework is successfully verified using a real-world case study.

Development of BIM-Based Bridge Maintenance System Considering Maintenance Data Schema and Information System

Due to the significant increase in the age of infrastructure globally, maintenance of existing structures has been prioritized over the construction of new structures, which are very costly. However, many infrastructure facilities have not been managed efficiently due to a lack of well-trained staff and budget limitations. Bridge management systems (BMSs) have been constructed and operated globally to maintain the originally designed structural performance and to overcome the inefficiency of maintenance practices for existing bridges. Unfortunately, because most of the current BMSs are based on 2D information systems, bridge maintenance data and information are not utilized effectively for bridge management. To overcome these problems, studies of BMSs based on building information modeling (BIM) have significantly increased in number. Most previous studies have proposed comprehensive frameworks containing approximate and limited information for maintenance to utilize BIM technology. Moreover, the utilization level of the maintenance information is less efficient because detailed information regarding safety diagnosis and maintenance are not included in data formats that are interpretable by computer algorithms. Therefore, in this study, a BIM-based BMS, including detailed information relating to safety diagnosis and maintenance, was constructed for the sustainability of bridge maintenance. To consider detailed information in the BMS, a maintenance data schema and its information system were established via the compilation of detailed information for safety diagnosis, repair and strengthening, remaining life, and valuation. In addition, a web data management program (WDMP) was developed using the maintenance data schema and information system, and was connected with the Midas CIM, which is a 3D modeling program. Finally, a prototype of the proposed BMS was established for an actual bridge in Korea. The proposed BMS in this study may be expected to improve the existing management practices for maintenance, and to reduce maintenance cost and information loss.

Sensing network security prevention measures of BIM smart operation and maintenance system

With the continuous expansion of network scale and the increasing complexity of attack methods, traditional network security protection equipment has been unable to cope with large-scale network security detection and protection. However, most current operation and maintenance systems cannot reasonably evaluate and predict network security. In order to be able to evaluate and prevent the network security of the bridge BIM intelligent operation and maintenance system under the background of big data, this paper uses the KDD Cup99 data set and the network attack data in the bridge BIM network environment to simulate the method proposed in this paper. The comparison results verify that the network security risk perception method proposed in this paper can realize network security risk perception more accurately and efficiently. This paper proposes a data mining method based on Bayesian network algorithm to evaluate the risk value of the bridge BIM intelligent operation and maintenance system. During the period from 0 to 110 min, the network risk value increased from 0.003 to 0.91. It can be seen that with the deepening of the attack phase, the degree of network risk will also increase. This paper uses the detection rate, false negative rate, false positive rate, AUC and other indicators to conduct simulation experiments on the data prediction-based network security risk prediction algorithm and comparison algorithm proposed in this paper. Simulation experiments show that under the four simulation experiment environments (pl = pe = 0.01/0.15, n = 20/50), the AUC of this scheme is increased by 0.018, 0.053, 0.008 and 0.11, respectively. The proposed algorithms are better than the comparison algorithms.

Performance Degradation Model for Concrete Deck of Bridge Using Pseudo-LSTM

The purpose of a bridge maintenance strategy is to make effective decisions by evaluating current performance and predicting future conditions of the bridge. The social cost because of the rapid increase in the number of decrepit bridges. The current bridge maintenance system relies on traditional man-power-based methods, which determine the bridge performance by employing a material deterioration model, and thus shows uncertainty in predicting the bridge performance. In this study, a new type of performance degradation model is developed using the actual concrete deck condition index (or grade) data of the general bridge inspection history database (1995–2017) on the national road bridge of the bridge management system in Korea. The developed model uses the long short-term memory algorithm, which is a type of recurrent neural network, as well as layer normalization and label smoothing to improve the applicability of basic data. This model can express the discrete historical degradation indices in continuous form according to the service life. In addition, it enables the prediction of bridge performance by using only basic information about new and existing bridges.

Development of a Bridge Inspection Support Robot System Using Two-Wheeled Multicopters

We developed a bridge inspection support robot system that uses a two-wheeled multicopter and 3D modeling technology. Our system is divided into three main parts: 1) a two-wheeled multicopter to capture close-up images of the inspection target, 2) damage extraction using 3D modeling technology, and 3) a 3D model-based bridge maintenance system that stores inspection information linked with the Industry Foundation Classes (IFC) 3D product model. An inspector can use this system to visually inspect the overall structure and accurately detect hairline cracks.

Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model

Preventive maintenance is increasingly becoming an essential strategy in the bridge industry owing to its proactive advantage of maintaining the structural sustainability during its entire service life. Several in-use bridges lack an appropriate regular maintenance solution, leading to extra cost during the operation stage. This paper proposes a new generation of the bridge maintenance system by using a digital twin model concept for more reliable decision-making. A detailed solution is proposed in this work to enhance the bridge maintenance process using a parallel solution: a maintenance information management system based on a 3D information model in conjunction with a digital inspection system using image processing. Three-dimensional digital models are required to utilise information from the entire lifecycle of a project, including design and construction, operation, and maintenance, by continuously exchanging and updating data from each stakeholder. For the maintenance of prestressed concrete bridges, the twin models are defined and their uses are presented.

Applications of GIS for Bridges Maintenance Service

AThe Bridge Maintenance Management System (BMMS) is an application system that uses existing data from a Bridge Management System database for monitoring and analysis of current bridges performance, as well as for estimating the current and future maintenance and rehabilitation needs of the bridges. In a transportation context, the maintenance management is described as a cost-effective process to operate, construct, and maintain physical money. This needs analytical tools to support the allocation of resources, materials, equipment, including personnel, and supplies. Therefore, Geographic Information System (GIS) can be considered as one tool to develop the road and bridge maintenance management system.This research aims to use GIS to create a database for bridge maintenance system. Two study areas have been chosen: the old construction bridge (Al-Qadisiyah) and the newly constructed bridge (Barboty). Both of them are in Al-Muthanna city\\Iraq. The creation of database was based on field surveying data. In order to monitor the damages and decline of the bridges, four periodically observations have been achieved. The first analysis of the observations was conducted by graphical analysis which gave the results between all the observations. The second part of analysis included the process of documenting the cracks observed in both study areas via hyperlink tool. This tool can display each of the bridge cracks in the form of window that contains full information for each crack such as, photo of the crack, station number of the crack, coordinates of the crack, type of crack, the crack description, and risk levels of the crack. This information may help to save cost, time and effort to decision makers in the relevant institutions.

BIM authoring for an image-based bridge maintenance system of existing cable-supported bridges

Infrastructure nowadays is increasingly become the main backbone for the metropolitan development in general. Along with the rise of new facilities, the demand in term of maintenance for the existing bridges is indispensable. Recently, the terminology of “preventive maintenance” is not unfamiliar with the engineer, literally is the use of a bridge maintenance system (BMS) based on a BIM-oriented model. In this paper, the process of generating a BMS based on BIM model is introduced in detail. Data management for this BMS is separated into two modules: site inspection system and information management system. The noteworthy aspect of this model lays on the closed and automatic process of “capture image, generate the technical damage report, and upload/feedback to the BMS” in real-time. A pilot BMS system for a cable-supported bridge is presented which showed a good performance and potential to further development of preventive maintenance.

Discovering regulatory concerns on bridge management: An author-topic model based approach

The management of bridges has major influence on the safety of transport system. Bridge management (BM) practices are carried out in compliance with policies and regulations. This study aims to identify the major aspects of BM from policy documents. We conducted a two-round retrieve to construct a comprehensive dataset of 263 related policies and regulations issued in the 31 provinces/municipalities of mainland China in recent 30 years. The Author-Topic Model (ATM) text mining approach was adopted to identify the key topics in the policies. The revealed 12 topics correspond to 12 major aspects of BM. Some topics, such as bridge maintenance and safety responsibility system, are gaining increasing attention in recent years, while the prevalence of some topics, such as bridge maintenance department configuration, is decreasing. The Granger causality tests suggest that remedial measures attract immediate regulatory attention in response to accidents, while proactive measures contribute to bridge collapse accident reduction, however, in a relatively long term. These findings provide practical implications for policy makers by reminding them that proactive measures should be emphasized alongside remedial measures.

Steel bridge long-term performance research technology framework and research progress

To ensure structural sustainability, it is necessary to conduct steel bridge long-term performance study, including bridge design, evaluation, maintenance, and reinforcement technology. The research on steel bridge long-term performance is introduced in four aspects: (1) fatigue performance experimental study for full-scale orthotropic steel bridge decks in laboratory to study its fatigue failure mechanism, in order to improve fatigue design methodology and find rational reinforcement and maintenance method; (2) conducting steel bridge out-of-plane distortion-induced fatigue performance study, and developing cold reinforcement method; (3) performance study for base material and typical joint under long-term vehicle and environmental effect in aging steel bridges, and safety assessment and maintenance of existing steel bridge; (4) temperature gradient monitoring for steel box girder model to build the temperature design mode. Meantime, in-situ tests and monitoring are conducted for steel bridge long-term performance detection, assessment, and maintenance. The study results in this article build the research framework of steel bridge long-term performance preliminarily, which is the basis for steel bridge sustainable design, maintenance, and cold reinforcement methodology system.

Current status and issues of the bridge maintenance system of municipalities affiliated to a prefecture

Current status and issues of the bridge maintenance system of municipalities affiliated to a prefecture

Development of a robotic bridge maintenance system

This paper will expand on the Robotic Bridge Maintenance System (RBMS) developed by the Construction Automation and Robotics Laboratory (CARL) at North Carolina State University (NCSU). The system consists of a 4- dof robot, designed and built at NCSU, mounted on the end of a truck-mounted peeper crane. Additionally, a containment system is mounted in front of the robot to contain the toxic waste created by the removal of the lead-based paint from the bridge beams and trusses.