Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted

Data-driven Approach to Support Bridge Asset Management

Economic growth and reduction of poverty lies in a well-planned, constructed, and maintained infrastructure that includes water and sanitation networks, airports, schools, health facilities, and highways systems. As bridges are an integral component of the nation’s highway system infrastructure, deficient bridges without timely maintenance may endanger the public and affect the economy on a broader scale. Currently, more than the 9.0% of the bridges in the U.S. are graded as structurally deficient, and the new estimate to address these bridges is $123 billion. Thus, in order to keep the level of safety and serviceability of these infrastructure assets, efforts in an accurate prediction of condition ratings, a better characterization of deficient bridges, and a focus on prioritization of deficient bridges can help. Currently, bridge stakeholders face budget constraints; thus, they need a systematic approach to better estimate maintenance budgets, make informed decisions in bridge design, and prioritize bridge maintenance. This dissertation research has two major objectives. The first objective is to provide a framework to predict and characterize superstructure deficiency. The second objective is to present a methodology to prioritize bridge maintenance. This dissertation used NBI databases as the main data source and utilized data mining techniques, multi-criteria decision analysis, and GIS to achieve the objectives of the study. Moreover, this dissertation follows a three-journal paper format. The first paper addresses the development of a framework to create predictive models of superstructure ratings for steel and prestressed concrete bridges. The second paper identifies a framework to characterize superstructure deficiency of steel bridges. The third paper presents a decision-making framework to prioritize bridge maintenance through using aggregate bridge ratings and average daily traffic (ADT). This dissertation contributes to the overall body of knowledge by establishing frameworks to develop reliable models to predict superstructure ratings, identify factors that accelerate superstructure deficiency, and prioritize bridge maintenance. The results of this dissertation can be used by any bridge stakeholder to complement their current bridge management programs.Civil Engineerin

Wireless Monitoring Systems for Long-Term Reliability Assessment of Bridge Structures based on Compressed Sensing and Data-Driven Interrogation Methods.

The state of the nation’s highway bridges has garnered significant public attention due to large inventories of aging assets and insufficient funds for repair. Current management methods are based on visual inspections that have many known limitations including reliance on surface evidence of deterioration and subjectivity introduced by trained inspectors. To address the limitations of current inspection practice, structural health monitoring (SHM) systems can be used to provide quantitative measures of structural behavior and an objective basis for condition assessment. SHM systems are intended to be a cost effective monitoring technology that also automates the processing of data to characterize damage and provide decision information to asset managers. Unfortunately, this realization of SHM systems does not currently exist. In order for SHM to be realized as a decision support tool for bridge owners engaged in performance- and risk-based asset management, technological hurdles must still be overcome. This thesis focuses on advancing wireless SHM systems. An innovative wireless monitoring system was designed for permanent deployment on bridges in cold northern climates which pose an added challenge as the potential for solar harvesting is reduced and battery charging is slowed. First, efforts advancing energy efficient usage strategies for WSNs were made. With WSN energy consumption proportional to the amount of data transmitted, data reduction strategies are prioritized. A novel data compression paradigm termed compressed sensing is advanced for embedment in a wireless sensor microcontroller. In addition, fatigue monitoring algorithms are embedded for local data processing leading to dramatic data reductions. In the second part of the thesis, a radical top-down design strategy (in contrast to global vibration strategies) for a monitoring system is explored to target specific damage concerns of bridge owners. Data-driven algorithmic approaches are created for statistical performance characterization of long-term bridge response. Statistical process control and reliability index monitoring are advanced as a scalable and autonomous means of transforming data into information relevant to bridge risk management. Validation of the wireless monitoring system architecture is made using the Telegraph Road Bridge (Monroe, Michigan), a multi-girder short-span highway bridge that represents a major fraction of the U.S. national inventory.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116749/1/ocosean_1.pd

Development of a Long-term, Multimetric Structural Health Monitoring System for a Historic Steel Truss Swing Bridge

The bridge stock across the United States is ageing, with many bridges approaching the end of their design life. The situation is so dire that the American Society of Civil Engineers gave the nation’s bridges a grade of “C+” in the 2013 edition of their Report Card on America’s Infrastructure. In fact, at the end of 2011, nearly a quarter of all bridges in the United States were classified as either structurally deficient or functionally obsolete. Thus, the nation’s bridges are in desperate need of rehabilitation and maintenance. However, limited funds are available for the repair of bridges. Management of the nation’s bridge infrastructure requires an efficient and effective use of available funds to direct the maintenance and repair efforts. Structural health monitoring has the potential to supplement the current routine of scheduled bridge inspections by providing an objective and detailed source of information about the status of the bridge. This research develops a framework for the long-term monitoring of bridges that leverages multimetric data to provide value to the bridge manager. The framework is applied to the Rock Island Arsenal Government Bridge. This bridge is a historic, steel truss, swing bridge that spans the Mississippi River between Rock Island, IL and Davenport, IA. The bridge is owned and operated by the US Army Corps of Engineers (USACE) and is a vital link for vehicular, train, and barge traffic. The USACE had a system of fiber optic strain gages installed on the bridge. As part of this research, this system was supplemented with a wireless sensor network that measured accelerations on the bridge. The multimetric data from the sensor systems was collected using a program developed in the course of this research. The data was then analyzed and metrics were developed that could be used to determine the health of the structure and the sensor networks themselves. Statistical process control methods were established to detect anomalous behavior in the short and long term time scales. Methods to locate and quantify the damage that has occurred in the structure once an anomaly has been detected were demonstrated. One of the methods developed as part of this research was a first order flexibility method. The SHM system this research develops has the desirable characteristics of being continuous temporally, multimetric, scalable, robust, autonomous, and informative. By necessity, some aspects of the developed SHM framework are unique and customized exclusively for the Rock Island Government Bridge. However, the principles developed in the framework are applicable to the development of an SHM system for any other bridge. Application of the SHM framework this research develops to other bridges has the potential to increase objectivity in the evaluation of bridges and focus maintenance efforts and funds on the bridges that are most critical to the public safety.Financial support for this research was provided in part by the Army Corps of Engineers Construction Engineering Research Laboratory (CERL) through a subcontract with Mandaree Enterprise Corporation.Ope

Global and Local Structural Health Monitoring Methods Based on Wireless Telemetry and Boundary-based Thermography

Our nation’s economy is dependent upon its transportation system for the movement of people, goods and services. Infrastructure plays a vital role in supporting transportation services. Given their importance, structures must be maintained to offer safe and reliable operations over extended life-cycles. Structural health monitoring (SHM) has emerged to offer owners a quantitative approach to monitoring structures, assessing system performance and estimating structural conditions. While SHM systems have been successfully deployed to structures, their full potential has not been reaped due to the gap that exists between SHM data and the decision-making needs of owners. This thesis contributes to the field by bridging this gap through two approaches. First, the thesis explores the advancement of wireless monitoring systems whose instrumentation strategy is defined by the needs of the decision-making process of the owner. This is illustrated in the thesis by exploring wireless monitoring systems and associated data-to-decision (D2D) frameworks in the United States Coast Guard (USCG) high-speed aluminum Response Boat-Medium (RB-M) and in the Harahan Bridge. In the former, the wireless hull monitoring system is tailored to derive RB-M hull response data over a short-period to create relationships between the environmental and operational conditions (EOC) of the vessel and the accumulation of fatigue in a critical hull component. In doing so, the vessel owner can make life-cycle decisions centered on managing fatigue accumulation by considering the future operational profile of the vessel. In the latter application, a wireless monitoring system is installed on the Harahan Bridge (which is a steel truss railroad bridge) to monitor bridge responses to triggered load events including trains, collisions, and earthquakes. Again, a fatigue critical eyebar element is considered with an alerting framework created to alert the bridge owner of overloading conditions that can accelerate fatigue accumulation. While the two case studies showcase clear benefits to designing wireless monitoring systems around the decision-making of the asset owner, they also highlight the value of local structural measurements for component health assessment. To extend the benefits offered by local sensing further, the thesis explores the creation of a cost-effective approach to damage detection through thermal conduction. Using point heaters and temperature sensors, a thermal-based computed tomography (CT) image reconstruction method is developed for two-dimensional (2D) mapping of structural conditions. This powerful local damage imaging method is implemented using a wireless impedance analyzer developed for use in structural wireless monitoring systems. In summary, a new approach to designing SHM systems is developed that looks first at the desired outcome, or decision that the data should inform. This is showcased in two unique wireless monitoring system and D2D framework studies. Next, a novel thermal imaging technique is proposed and validated. Lastly, a first-of-its-kind multi-functional wireless impedance analyzer is developed that is capable of enabling the wireless and permanent installation of multiple spatial sensing techniques.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138654/1/nephi_1.pd

Global Dynamic Characterization and Load Rating of Bridge Structures Utilizing Economical Dynamic Excitation Devices

Experimental modal analysis (EMA) of bridges and other civil structures can be used to acquire quantitative data describing their condition, and enhance opportunities for structural health monitoring and related fields. The use of EMA on civil structures has been limited by the high cost of the excitation devices that are required for the best data quality. The objective of the research reported herein is to evaluate a low-cost excitation system for multiple-input, multiple-output (MIMO) EMA, enabling the production of accurate estimates of the global behavior of in-service bridges. The prototype excitation system is composed of consumer entertainment devices, namely tactile transducers and subwoofer amplifiers, which are capable of providing excitation in the range of 5 Hz to 200 Hz. The use of these devices in vibration testing is unprecedented, and their low cost allows approximately 30 structural degrees-of-freedom to be excited for the price of a single purpose-built laboratory shaker device. Methods are developed to systematically characterize the operational performance of the devices. Research and testing are also performed to optimize the techniques by which the system can be used for MIMO EMA of bridges. The excitation system is then validated for MIMO EMA by dynamically characterizing a large-scale laboratory structure and comparing the results to those from traditional excitation methods. The system is then deployed on an in-service highway bridge, representing the first time that more than two shakers have been used in MIMO EMA testing of a bridge. The identification results using MIMO EMA are shown to be superior to those found using traditional excitation methods. Finally, the identified modal parameters of the in-service bridge are used in load rating. Direct use of the modal properties of a bridge for load rating is unprecedented, and a relatively simple method to use measured modal flexibility to help determine live load demand is developed herein. The bridge load ratings calculated from the new method are compared to traditional methods. In summary, a low-cost excitation system is optimized and systematically evaluated for MIMO EMA testing of civil structures, and the use of the system is validated in the laboratory and in the field. A new method to improve bridge rating reliability is then developed using the high quality modal parameters found via the optimized testing process

Railway bridge condition monitoring and fault diagnostics

The European transportation network is ageing continuously due to environmental threats, such as traffic, wind and temperature changes. Bridges are vital assets of the transportation network, and consequently their safety and availability need to be guaranteed to provide a safe transportation network to passenger and freights traffic. The main objective of this thesis is to develop a bridge condition monitoring and damage diagnostics method. The main element of the proposed Structural Health Monitoring (SHM) method is to monitor and assess the health state of a bridge continuously, by taking account of the health state of each element of the bridge. In this way, an early detection of the ongoing degradation of the bridge can be achieved, and a fast and cost-effective recovery of the optimal health state of the infrastructure can be achieved. A BBN-based approach for bridge condition monitoring and damage diagnostics is proposed and developed to assess and update the health state of the bridge continuously, by taking account of the health state of each element of the bridge. At the same time, the proposed BBN approach allows to detect and diagnose damage of the bridge infrastructure. Firstly, the BBN method is developed for monitoring the condition of two bridges, which are modelled via two Finite Element Models (FEMs). The Conditional Probability Tables (CPTs) of the BBN are defined by using an expert knowledge elicitation process. Results shows that the BBN allows to detect and diagnose damage of the bridges, however the performance of the BBN can be improved by pre-processing the data of the bridge behaviour and improving the definition of the CPTs. A data analysis methodology is then proposed to pre-process the data of the bridge behaviour, and to use the results of the analysis as an input to the BBN. The proposed data analysis methodology relies on a five-step process: i) remove of the outlier of the bridge data; ii) identify of the free-vibration of the bridge; iii) extract statistical, frequency-based and vibration -based features from the free-vibration behaviour of the bridge; iv) assess the features trend over time, by using the extracted features as an input to an Empirical Mode Decomposition (EMD) algorithm; v) evaluate of the Health Indicator (HI) of the bridge element. The proposed data analysis methodology is tested on two in-field bridges, a steel truss bridge and a post-tensioned concrete bridge, which are subject to a progressive damage test. A machine learning method is also developed in order to assess the health state of the bridge automatically. A Neuro Fuzzy Classifier (NFC) is adopted for this purpose. The results of the NFC can potentially be used as an input to the BBN nodes, to select the states of the BBN nodes, and improve the BBN performance. In fact, the NFC shows high accuracy in assessing the health state of bridge elements. An optimal set of HIs, which allows to maximize the accuracy of the NFC, is identified by adopting an iterative Modified Binary Differential Evolution (MBDE) method. The NFC is applied to the post-tensioned concrete in-field bridge that is subject to a progressive damage test. Hence, the performance of the BBN is improved significantly by pre-processing the bridge data, but also by developing a novel method to continuously update the CPTs of the BBN. The CPTs update process relies on the actual health state of the bridge element, and the knowledge of bridge engineers. Indeed, the CPT updating method aims to merge the expert knowledge with the analysis of the bridge behaviour. In this way, the diagnostic ability of the BBN is improved by merging the expertise of bridge engineer, who can analyse hypothetical damage scenarios of the bridge, and the analysis of a database of known bridge behaviour in different health states. The method is verified on the post-tensioned concrete in-field bridge, by developing a BBN to monitor the health state of the bridge continuously. The damages of the bridge are diagnosed by the proposed BBN. Finally, a method to analyse database of unknown infrastructure behaviour is finally proposed. An ensemble-based change-point detection method is presented to analyse a database of past unknown infrastructure behaviour. The method aims to identify the most critical change of the health state of the infrastructure, by providing the characteristics of such a change, in terms of time duration and possible causes. The method is applied to a database of tunnel behaviour, which is subject to renewal activities that influence the health state of the infrastructure

Railway bridge condition monitoring and fault diagnostics

The European transportation network is ageing continuously due to environmental threats, such as traffic, wind and temperature changes. Bridges are vital assets of the transportation network, and consequently their safety and availability need to be guaranteed to provide a safe transportation network to passenger and freights traffic. The main objective of this thesis is to develop a bridge condition monitoring and damage diagnostics method. The main element of the proposed Structural Health Monitoring (SHM) method is to monitor and assess the health state of a bridge continuously, by taking account of the health state of each element of the bridge. In this way, an early detection of the ongoing degradation of the bridge can be achieved, and a fast and cost-effective recovery of the optimal health state of the infrastructure can be achieved. A BBN-based approach for bridge condition monitoring and damage diagnostics is proposed and developed to assess and update the health state of the bridge continuously, by taking account of the health state of each element of the bridge. At the same time, the proposed BBN approach allows to detect and diagnose damage of the bridge infrastructure. Firstly, the BBN method is developed for monitoring the condition of two bridges, which are modelled via two Finite Element Models (FEMs). The Conditional Probability Tables (CPTs) of the BBN are defined by using an expert knowledge elicitation process. Results shows that the BBN allows to detect and diagnose damage of the bridges, however the performance of the BBN can be improved by pre-processing the data of the bridge behaviour and improving the definition of the CPTs. A data analysis methodology is then proposed to pre-process the data of the bridge behaviour, and to use the results of the analysis as an input to the BBN. The proposed data analysis methodology relies on a five-step process: i) remove of the outlier of the bridge data; ii) identify of the free-vibration of the bridge; iii) extract statistical, frequency-based and vibration -based features from the free-vibration behaviour of the bridge; iv) assess the features trend over time, by using the extracted features as an input to an Empirical Mode Decomposition (EMD) algorithm; v) evaluate of the Health Indicator (HI) of the bridge element. The proposed data analysis methodology is tested on two in-field bridges, a steel truss bridge and a post-tensioned concrete bridge, which are subject to a progressive damage test. A machine learning method is also developed in order to assess the health state of the bridge automatically. A Neuro Fuzzy Classifier (NFC) is adopted for this purpose. The results of the NFC can potentially be used as an input to the BBN nodes, to select the states of the BBN nodes, and improve the BBN performance. In fact, the NFC shows high accuracy in assessing the health state of bridge elements. An optimal set of HIs, which allows to maximize the accuracy of the NFC, is identified by adopting an iterative Modified Binary Differential Evolution (MBDE) method. The NFC is applied to the post-tensioned concrete in-field bridge that is subject to a progressive damage test. Hence, the performance of the BBN is improved significantly by pre-processing the bridge data, but also by developing a novel method to continuously update the CPTs of the BBN. The CPTs update process relies on the actual health state of the bridge element, and the knowledge of bridge engineers. Indeed, the CPT updating method aims to merge the expert knowledge with the analysis of the bridge behaviour. In this way, the diagnostic ability of the BBN is improved by merging the expertise of bridge engineer, who can analyse hypothetical damage scenarios of the bridge, and the analysis of a database of known bridge behaviour in different health states. The method is verified on the post-tensioned concrete in-field bridge, by developing a BBN to monitor the health state of the bridge continuously. The damages of the bridge are diagnosed by the proposed BBN. Finally, a method to analyse database of unknown infrastructure behaviour is finally proposed. An ensemble-based change-point detection method is presented to analyse a database of past unknown infrastructure behaviour. The method aims to identify the most critical change of the health state of the infrastructure, by providing the characteristics of such a change, in terms of time duration and possible causes. The method is applied to a database of tunnel behaviour, which is subject to renewal activities that influence the health state of the infrastructure

6th International Conference on Mechanical Models in Structural Engineering

Producción CientíficaThis ebook contains the 37 full papers submitted to the 6th International Conference on Mechanical Models in Structural Engineering (CMMOST 2021) held in Valladolid on December 2021