Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

Damage identification in bridge structures : review of available methods and case studies

Bridges are integral parts of the infrastructure and play a major role in civil engineering. Bridge health monitoring is necessary to extend the life of a bridge and retain safety. Periodic monitoring contributes significantly in keeping these structures operational and extends structural integrity. Different researchers have proposed different methods for identifying bridge damages based on different theories and laboratory tests. Several review papers have been published in the literature on the identification of damage and crack in bridge structures in the last few decades. In this paper, a review of literature on damage identification in bridge structures based on different methods and theories is carried out. The aim of this paper is to critically evaluate different methods that have been proposed to detect damages in different bridges. Different papers have been carefully reviewed, and the gaps, limitations, and superiority of the methods used are identified. Furthermore, in most of the reviews, future applications and several sustainable methods which are necessary for bridge monitoring are covered. This study significantly contributes to the literature by critically examining different methods, giving guidelines on the methods that identify the damages in bridge structures more accurately, and serving as a good reference for other researchers and future works

Baseline Models for Bridge Performance Monitoring

A baseline model is essential for long-term structural performance monitoring and evaluation. This study represents the first effort in applying a neural network-based system identification technique to establish and update a baseline finite element model of an instrumented highway bridge based on the measurement of its traffic-induced vibrations. The neural network approach is particularly effective in dealing with measurement of a large-scale structure by a limited number of sensors. In this study, sensor systems were installed on two highway bridges and extensive vibration data were collected, based on which modal parameters including natural frequencies and mode shapes of the bridges were extracted using the frequency domain decomposition method as well as the conventional peak picking method. Then an innovative neural network is designed with the input being the modal parameters and the output being the structural parameters of a three-dimensional finite element model of the bridge such as the mass and stiffness elements. After extensively training and testing through finite element analysis, the neural network became capable to identify, with a high level of accuracy, the structural parameter values based on the measured modal parameters, and thus the finite element model of the bridge was successfully updated to a baseline. The neural network developed in this study can be used for future baseline updates as the bridge being monitored periodically over its lifetime

Structural Health Monitoring and Damage Evaluation of Full-Scale Bridges Using Triaxial Geophones: Controlled In-Situ Experiments and Finite Element Modeling

The purpose of this study was to evaluate the effectiveness of various vibration-based damage detection methods using triaxial vibration records obtained using inexpensive geophones during in-situ, full-scale, damaged bridge tests. Geophones are passive directional sensors and much cheaper than accelerometers which are typically used for structural vibration measurements. However, magnitude and phase errors associated with a geophone’s output must be corrected for if they are implemented in bridge monitoring systems. This research discusses correction procedures for magnitude and phase errors associated with geophones. A simply supported beam was analyzed to verify that the correction procedures and modal parameter identification procedures used produced reliable results. A full-scale bridge test was also performed to further validate the correction and modal analysis procedures used. The results of the simple beam and full-scale bridge tests were validated using finite element modeling. Vibration-based damage detection relies on changes in the dynamic properties of a structure to detect damage. Only one other study was found that compares various vibration-based damage detection techniques using full-scale damaged bridge tests. Thus, a need remains for further comparison of vibration-based damage detection techniques using vibration data collected entirely on full-scale bridges. This study compares various vibration-based damage detection techniques using triaxial vibration records obtained during separate in-situ, full-scale, damaged bridge tests. Furthermore, the damage detection techniques are extended to three dimensions to evaluate three dimensional response of the bridge to damage. This is a unique aspect of the current research because no other three dimensional data sets obtained from in-situ, full-scale, damaged bridge tests have been reported in the literature. Finite element modeling is perhaps the most widely relied upon method of structural and mechanical analysis. In the field of vibration-based damage detection, finite element models are often used to plan field tests, to verify field test results, and to produce damaged data sets when the actual structure is unable to be damaged. As part of this research, finite element models were constructed to lend credibility to the field test results and to investigate damage scenarios other than those inflicted during the field tests

Terrestrial Laser Scanning-Based Bridge Structural Condition Assessment

Objective, accurate, and fast assessment of a bridge’s structural condition is critical to the timely assessment of safety risks. Current practices for bridge condition assessment rely on visual observations and manual interpretation of reports and sketches prepared by inspectors in the field. Visual observation, manual reporting, and interpretation have several drawbacks, such as being labor intensive, subject to personal judgment and experience, and prone to error. Terrestrial laser scanners (TLS) are promising sensors for automatically identifying structural condition indicators, such as cracks, displacements, and deflected shapes, because they are able to provide high coverage and accuracy at long ranges. However, limited research has been conducted on employing laser scanners to detect cracks for bridge condition assessment, and the research has mainly focused on manual detection and measurement of cracks, displacements, or shape deflections from the laser scan point clouds. This research project proposed to measure the performance of TLS for the automatic detection of cracks for bridge structural condition assessment. Laser scanning is an advanced imaging technology that is used to rapidly measure the three-dimensional (3D) coordinates of densely scanned points within a scene. The data gathered by a laser scanner are provided in the form of point clouds, with color and intensity data often associated with each point within the cloud. Point cloud data can be analyzed using computer vision algorithms to detect cracks for the condition assessment of reinforced concrete structures. In this research project, adaptive wavelet neural network (WNN) algorithms for detecting cracks from laser scan point clouds were developed based on the state-of-the-art condition assessment codes and standards. Using the proposed method for crack detection would enable automatic and remote assessment of a bridge’s condition. This would, in turn, result in reducing the costs associated with infrastructure management and improving the overall quality of our infrastructure by enhancing maintenance operations

Study on bridge inspections, A: identifying barriers to new practices and providing strategies for change

2021 Summer.Includes bibliographical references.Bridge inspections are one of the key elements required for a successful bridge management process to ensure adequate bridge performance. Inspections significantly inform maintenance decisions and can help in managing maintenance activities to achieve a reliable bridge network. In the United States (U.S.) routine visual inspections are required for most bridges at a maximum interval of 24-months regardless of the bridge condition. However, limitations of current bridge inspection practices impact the quality of information provided about bridge condition and the subsequent decisions made based on that information. Accordingly, the overarching goal of this research project is to support bridge inspection practices by providing a systematic and rational framework for bridge inspection planning and identifying the factors that can facilitate innovation and research transfer in the bridge inspection field. To do so, this dissertation includes three separate yet related studies; each focusing on essential aspects of bridge inspection planning. Much research in bridge inspection has been conducted to improve the inspection planning process. The first study provides an overview of current bridge inspection practices in the U.S. and conducts a systematic literature review on innovations in the field of bridge inspection planning to identify research gaps and future needs. This study provides a background on the history of bridge inspection in the U.S., including current bridge inspection practices and their limitations, and analyzes the connections between nondestructive evaluation techniques, deterioration models and bridge inspection management. The primary emphasis of the first study is a thorough analysis of research proposing and investigating different methodologies for inspection planning. Studies were analyzed and categorized into three main types of inspection planning approaches; methods that are based on: reliability, risk analysis, and optimization approaches. This study found that one of the main barriers that may be preventing the implementation of new inspection planning frameworks in practice is that the approaches presented focus on a single bridge element or deterioration mechanism in the decision-making process. Additionally, it was concluded that approaches in the literature are either complex to apply or depend solely on expert judgement. Limitations of the uniform calendar-based approach used to schedule routine inspections have been reported in the literature. Accordingly, the objective of the second study is to provide a new systematic approach for inspection planning that integrates information from bridge condition prediction models, inspection data, and expert opinion using Bayesian analysis to enhance inspection efficiency and maintenance activities. The proposed uncertainty-based inspection framework can help bridge owners avoid unnecessary or delayed inspections and repair actions, determine the inspection method, and consider more than one deterioration process or bridge component during the inspection planning process. The inspection time and method are determined based on the uncertainty and risks associated with the bridge condition. As uncertainty in the bridge condition reaches a defined threshold, an inspection is scheduled utilizing nondestructive techniques to reduce the uncertainty level. The framework was demonstrated on a new and on an existing reinforced concrete bridge deck impacted by corrosion deterioration. The results showed that the framework can reduce the number of inspections compared to conventional scheduling methods, while also reducing the uncertainty regarding the transition in the bridge deck condition and repair time. As identified through the first study, over the last two decades many researchers have focused on providing new ideas to improve conventional bridge inspection practices, however, little guidance is provided for implementing these new research products in practice. This, along with resistance to change and complexity of the proposed ideas, resulted in a lack of consistency and success in applying new technologies in bridge inspection programs across state departments of transportation (DOTs). Accordingly, the third paper presents a qualitative study set out to identify the factors that can help improve research products and accelerate change and research transfer in bridge inspection departments. This study used semi-structured interviews, written interviews, and questionnaires for data collection and engaged with twenty-six bridge staff members from different DOTs. The findings of this study are expected to be both specific to changes in bridge inspection practice and have some generalizability to other significant changes to engineering practice at DOTs. To improve research products, this study suggested that researchers need to collaborate more with DOT staff members and provide relevant research products that are not specific to certain bridge cases and can be applied on different bridges. Also, to facilitate change in transportation organizations, change leaders should focus on showing the need for change, gaining support from the FHWA, allocating the required resources, and enhancing the capacity of DOT staff members through training and effective communication. The investigation also presented participants' opinions on some of the aspects related to conventional inspection practices such as their support of a uniform inspection interval over a variable interval, and the main barriers limiting the use of NDE methods. This study contributes to the body of knowledge in the bridge inspection field by providing a new inspection planning approach that depends on the uncertainty and the risks associated with the bridge condition and uses both computational methods and expert judgment allowing bridge owners select inspection time and method while considering more than one deterioration process or bridge element. In addition, this study presents some of the factors that can help reduce the gap between research and practice and facilitate innovation and change in transportation organizations