Structural health monitoring of bridges for improving transportation security

Structural health monitoring (SHM) is a promising technology for determining the condition of significant transportation structures objectively for efficient management and preservation of transportation assets. In addition to identifying, locating, and quantifying damage and deterioration due to effects of operation, aging, and natural hazards, the need for taking terrorism-related hazards into account has become evident after 9/11 terrorist attacks. Key transportation facilities like major bridges were identified by Department of Homeland Security (DHS) as possible terrorist targets since their loss or even temporary deficiency could lead to major impacts on economy and mobility. Several governmental, local, and private organizations have been working on identifying possible modes of threats, determining and sorting vulnerable structures, and establishing ways to prevent, detect and respond to such attacks. Authorities are also investigating ways to integrate current and future bridge management systems with security surveillance systems. Highway bridges are key links of the transportation system. This paper reviews security measures for bridges and discuss possible integration of structural health and security monitoring for improving security and safety of bridges and emergency management after a natural or man-made disaster

Monitoring Technologies for Smart Cities and Civil Infrastructure Systems

The proportion of urban population in the world is expected to increase from 54% currently to 70% by 2050. A majority of Americans also reside in urban regions - according to the 2010 census 80% of Americans reside in urban areas. Given the large number of urban citizens in the world (and US) it is imperative that we identify solutions to improve the quality of life for urban residents and economic vitality of our cities. Studies to address and fulfill the needs of envisioned future smart city infrastructure should successfully integrate a range of engineering, humanities and sociological fields such as emerging communication technologies, Internet of Things (IoT), cyber security, cloud computing, intelligent transportation, infrastructure monitoring, analyzing tourism, theorizing structures of government and bureaucracy, project financing, public policy development and implementation. In this talk, we will first three overarching themes: (1) technologically advanced infrastructure with sensing and communication capability, (2) urban operations and services improved with better decisions using multilayered “big data”, and (3) utilization of technology for social, public policy, planning and governance to improve urban quality of life. Next, we will present a sampling of relevant U.S. research and education achievements in structural control and monitoring as compiled by U.S. Panel that are envisioned as concepts for smart cities. Finally, we will present our recent work at UCF CITRS in the area of structural health monitoring where novel technologies such as computer vision, deep learning have been developed for our existing and next generation of smart city infrastructure

Evaluation Of A System Identification Method For Structural Health Monitoring: Theory And Examples

In this paper, the authors explore implementing an identification technique to real life structural health monitoring problems. The method presented in this paper is Observer/Kalman IDentification (OKID) which is used in conjunction with Eigensystem Realization Algorithm (ERA). After a review of theoretical background, the method is applied to two laboratory tests. In the first case, the authors analyze dynamic test results of three reinforced concrete beams with different fiber reinforced polymer configurations. While the beams are statically loaded until failure multiple input and multiple output data sets were collected and correlation between the capacity and the dynamic properties is explored. For the second case dynamic test data of a steel grid is analyzed. The grid is tested without any damage for both single span and two span configurations and dynamic properties identified using OKID/ERA are compared with their analytical counterparts

Ambient Vibration Data Analysis For Structural Identification And Global Condition Assessment

System identification is an area which deals with developing mathematical models to characterize the input-output behavior of an unknown system by means of experimental data. Structural health monitoring (SHM) provides the tools and technologies to collect and analyze input and output data to track the structural behavior. One of the most commonly used SHM technologies is dynamic testing. Ambient vibration testing is a practical dynamic testing method especially for large civil structures where input excitation cannot be directly measured. This paper presents a conceptual and reliable methodology for system identification and structural condition assessment using ambient vibration data where input data are not available. The system identification methodology presented in this study is based on the use of complex mode indicator functions (CMIFs) coupled with the random decrement (RD) method to identify the modal parameters from the output only data sets. CMIF is employed for parameter identification from the unscaled multiple-input multiple-output data sets generated using the RD method. For condition assessment, unscaled flexibility and the deflection profiles obtained from the dynamic tests are presented as a conceptual indicator. Laboratory tests on a steel grid and field tests on a long-span bridge were conducted and the dynamic properties identified from these tests are presented. For demonstrating condition assessment, deflected shapes obtained from unscaled flexibility are compared for undamaged and damaged laboratory grid structures. It is shown that structural changes on the steel grid structure are identified by using the unscaled deflected shapes. © 2008 ASCE

Damage Assessment With Ambient Vibration Data Using A Novel Time Series Analysis Methodology

In this study, a novel approach using a modified time series analysis methodology is used to detect and locate structural changes by using ambient vibration data. In addition, it is shown that the level of the damage feature gives important information about the relative change of the damage severity, although direct damage quantification is not achieved. In this methodology, random decrement (RD) is used to obtain pseudofree response data from the ambient vibration time histories. Autoregressive models with exogenous input (ARX models) are created for different sensor clusters by using the pseudofree response of the structure. The output of each sensor in a cluster is used as an input to the ARX model to predict the output of the reference channel of that sensor cluster. After creating ARX models for the healthy structure for each sensor cluster, these models are used for predicting the data from the damaged structure. The difference between the fit ratios is used as the damage feature. The methodology is first applied to experimental ambient vibration data from a steel grid structure, in which different damage scenarios, such as local stiffness loss and boundary condition change, are simulated. The results show that damage was detected and located successfully for most of these cases. Moreover, it is observed that the relative extent of the damage is also estimated by using the method. Then, output-only data from the Z24 bridge is used for further verification of the methodology with real-life data where different levels of pier settlement were applied as damage. It is shown that the approach is successful in damage identification and localization with a minimum number of false alarms. The potential and advantages of the methodology are discussed on the basis of the experimental results. Limitations of the approach are also addressed, along with future research directions. © 2011 American Society of Civil Engineers

Signal Processing For Sensing And Monitoring Of Civil Infrastructure Systems

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Laboratory Benchmark Studies For Health Monitoring And Condition Assessment Of Civil Infrastructure Systems

Successful health monitoring applications on real structures can be achieved by integrating experimental, analytical and information technologies on real life operating structures. However, real-life investigations must be backed up by laboratory benchmark studies. Laboratory benchmark studies are critical for validating theory, concepts, and new technologies as well as creating a collaborative environment between different researchers. In addition, well-designed laboratory studies are a pre-requisite for field research. To implement these technologies, the writers have developed a physical bridge model in the laboratory. Data acquisition systems, accelerometers, a dynamic shaker, impact hammer, strain gages, tiltmeters, and static loads are all integrated for use on the physical model. In this paper, the writers will first discuss the issues related to the development of the physical model. Then, the damage scenarios to be simulated in the laboratory will be discussed. Finally, preliminary analysis results along with experimental counterparts are presented

Some Issues In Condition Assessment Of Large Structures Using Dynamic Measurements

The purpose of this paper is to present an overview of experimental structural dynamics as a unique technology for global health monitoring of large structures. Assessment of damage and objective condition evaluation of existing civil infrastructure systems (CIS) are important needs for making decisions during regular operation as well as before and after disasters. Objective condition assessment is also a fundamental knowledge need for successful health monitoring of CIS. In this paper, the writers discuss promises as well as the challenges for dynamic measurements, and the use of modal flexibility matrices for condition assessment when dynamic methods are applied on large structures. They present snapshots from their past and current studies where dynamic tests were implemented on medium and long span bridges

A Machine Learning Framework For Automated Functionality Monitoring Of Movable Bridges

Functionality of movable bridge highly depends on the performance of the mechanical components including gearbox and motor. Therefore, on-going maintenance of these components are extremely important for uninterrupted operation of movable bridges. Unfortunately, there have been only a few studies on monitoring of mechanical components of movable bridges. As a result, in this study, a statistical framework is proposed for continuous maintenance monitoring of the mechanical components. The efficiency of this framework is verified using long-term data that has been collected from both gearbox and motor of a movable bridge. In the first step, critical features are extracted from massive amount of Structural Health Monitoring (SHM) data. Next, these critical features are analyzed using Moving Principal Component Analysis (MPCA) and a condition-sensitive index is calculated. In order to study the efficiency of this framework, critical maintenance issues have been extracted from the maintenance reports prepared by the maintenance personnel and compared against the calculated condition index. It has been shown that there is a strong correlation between the critical maintenance actions, reported individually by maintenance personnel, and the condition index calculated by proposed framework and SHM data. The framework is tested for the gearbox