Ricardo Zaurin

Sensor Networks, Computer Imaging, and Unit Influence Lines for Structural Health Monitoring: Case Study for Bridge Load Rating

In this paper, a novel methodology for structural health monitoring of a bridge is presented with implementations for bridge load rating using sensor and video image data from operating traffic. With this methodology, video images are analyzed by means of computer vision techniques to detect and track vehicles crossing the bridge. Traditional sensor data are correlated with computer images to extract unit influence lines (UILs). Based on laboratory studies, UILs can be extracted for a critical section with different vehicles by means of synchronized video and sensor data. The synchronized computer vision and strain measurements can be obtained for bridge load rating under operational traffic. For this, the following are presented: a real life bridge is instrumented and monitored, and the real-life data are processed under a moving load. A detailed finite-element model (FEM) of the bridge is also developed and presented along with the experimental measurements to support the applicability of the approach for load rating using UILs extracted from operating traffic. The load rating of the bridges using operational traffic in real life was validated with the FEM results of the bridge and the simulation of the operational traffic on the bridge. This approach is further proven with different vehicles captured with video and measurements. The UILs are used for load rating by multiplying the UIL vector of the critical section with the load vector from the HL-93 design truck. The load rating based on the UIL is compared with the FEM results and indicates good agreement. With this method, it is possible to extract UILs of bridges under regular traffic and obtain load rating efficiently.

Structural health monitoring using video stream, influence lines, and statistical analysis:

Civil infrastructure systems experience damage, overloading, aging due to normal operations, severe environmental conditions, and extreme events. These effects change the structural behavior and performance. Novel structural health monitoring (SHM) strategies are increasingly becoming more important to objectively determine the actual condition and these changes. The main objective of this study is to demonstrate the integration of video images and sensor data as promising techniques for the safety of bridges in the context of SHM. The UCF 4-span bridge model is used to demonstrate the method. Image and sensing data are analyzed to obtain unit influence line (UIL) as an index for monitoring the bridge behavior under loading conditions identified using computer vision techniques. UILs are extracted for several different moving loads. In addition to the analysis of UILs in a comparative fashion, a new method based on statistical outlier detection from UIL vector sets is proposed and demonstrated. The new method is applied to detect and identify some of the most common damage scenarios for bridges such as changes in boundary conditions and loss of connectivity between composite sections. Successful results are obtained from the experimental studies.

Hybrid Sensor-Camera Monitoring for Damage Detection: Case Study of a Real Bridge

AbstractThis article presents the real-world implementation of a novel monitoring system in which video images and conventional sensor network data are simultaneously analyzed to detect possible damage on a movable bridge. The monitoring system was designed to detect such problems at the onset of damage. A video stream of traffic is processed to detect and classify vehicles to determine the vehicle load and location, while strain measurements are simultaneously collected at various critical locations on the bridge for both normal and damage conditions. A series of unit influence lines can then be extracted for all of the scenarios using the image and sensor data. Because large data sets result from continuous monitoring, the system also includes a statistical outlier-detection algorithm. The proposed methodology was successfully used to detect and locate common damage scenarios on a real-world bascule bridge.

Critical issues, condition assessment and monitoring of heavy movable structures: emphasis on movable bridges

In this paper, a relatively less studied class of structures is presented based on the research conducted on Floridas movable bridges over the last several years. Movable bridges consist of complex structural, mechanical and electrical systems that provide versatility to these bridges, but at the same time, create intermittent operational and maintenance challenges. Movable bridges have been designed and constructed for some time; however, there are fewer studies in the literature on movable bridges as compared to other bridge types. In addition, none of these studies provide a comprehensive documentation of issues related to the condition of movable bridge populations in conjunction with possible monitoring applications specific to these bridges. This paper characterises and documents these issues related to movable bridges considering both the mechanical and structural components. Considerations for designing a monitoring system for movable bridges are also presented based on inspection reports and expert opinions. The design and implementation of a monitoring system for a representative bascule bridge are presented along with long-term monitoring data. Various movable bridge characteristics such as opening/closing torque, bridge balance and friction are shown since these are critical for maintenance applications on mechanical components. Finally, the impact of environmental effects (such as wind and temperature) on bridge mechanical characteristics is demonstrated by analysing monitoring data for more than 1000 opening/closing events.

Structural Health Monitoring of Bridges: Fundamentals, Application Case Study and Organizational Considerations

F. Necati Catbas, University of Central Florida, Orlando, FL, USA, catbas@mail.ucf.edu Mustafa Gul, University of Central Florida, Orlando, FL, USA, mgul@pegasus.cc.ucf.edu Ricardo Zaurin, University of Central Florida, Orlando, FL, USA, ricardozaurin@hotmail.com Thomas Terrell, University of Central Florida, Orlando, FL, USA, tomkterrell@yahoo.com Yunus Dere, University of Central Florida, Orlando, FL, USA, dereyunus@gmail.com Marcus H. Ansley, Structures Research Center, Tallahassee,FL,USA,marc.ansley@dot.state.fl.us Dan M. Frangopol, Lehigh University, Bethlehem, PA, USA, dan.frangopol@lehigh.edu Kirk A. Grimmelsman, University of Arkansas, Fayetteville, AR, USA kgrimmel@uark.edu

Computer Vision for Structural Health Monitoring and Damage Detection of Bridges

Structural performance of Civil Infrastructure Systems (CIS) often decreases due to reasons such as damage, over loading, severe environmental conditions, and aging due to normal continued use. These effects will result in long-term structural damage and deterioration. As a result, novel Structural Health Monitoring (SHM) strategies are increasingly becoming more important. In this paper, integrated use of video images and sensor data in the context of SHM is demonstrated as promising technologies for safety and security of bridges. The synchronized image and sensing data are analyzed to obtain Unit Influence Line (UIL) as an index for monitoring bridge behavior under identified loading conditions. The UCF 4-span bridge model is used to explore the use of imaging devices and traditional sensing technology with UIL for damage detection. Different damage scenarios such as changes in boundary conditions and loss of connectivity between composite sections are analyzed. Experimental data is processed by means of statistical methods. Outlier detection algorithms are used to identify structural changes in large data sets obtained by monitoring and results are presented. Finally, advantages and disadvantages of the method are discussed.

Heavy Movable Structure Health Monitoring: A Case Study with a Movable Bridge in Florida

A large number of movable bridges exist in Florida. These movable bridges are at critical intersection points of highway and marine traffic. Since these structures are composed of structural, mechanical and electrical components, encountered maintenance problems are different in nature and are observed more frequently. Therefore, movable bridge rehabilitation and maintenance costs are considerably higher than those of fixed bridges. The main issues are the deterioration due to their proximity to waterways, mechanical system failures, and fatigue due to the stress fluctuations during the operation. To improve their maintenance and predict possible problems ahead of time, continuous monitoring of these structures can be considered as a promising approach. In this study, the authors first discuss the problems and monitoring needs of such bridges. In the second part, design of the sensor network, data acquisition setup and data analysis methodologies are reviewed. Then, the field implementation and related challenges are described for a particular bridge. Finally, preliminary sample results from the analysis of the field data are discussed from safety, operation and maintenance point of view.

Use of Statistical Analysis, Computer Vision and Reliability for Structural Health Monitoring

Structural health monitoring (SHM) of civil infrastructures is becoming more feasible with the help of recent developments in sensing and computing technologies being more available and affordable. The SHM system may contain various types of measurements including, but not limited to, vibration, strain and image data. In this paper, the authors provide a general discussion of the two critical aspects of SHM: assessment of the current condition and future performance prediction from their recent studies at the University of Central Florida. First, SHM data can be used to track and evaluate the current condition of the structure with the help of statistical pattern recognition algorithms and computer vision techniques. Statistical analysis of these types of data can provide rapid extraction of information about the changes in structural behavior whereas the use of the computer vision technologies in a monitoring system offers to detect events visually. Subsequently, the available information obtained can be used for decision-making about the future performance of the structure. Prediction of the future performance is a very crucial step in better managing the life cycle safety, serviceability and costs.