Computer Vision Based Structural Identification Framework for Bridge Health Mornitoring

The objective of this dissertation is to develop a comprehensive Structural Identification (St-Id) framework with damage for bridge type structures by using cameras and computer vision technologies. The traditional St-Id frameworks rely on using conventional sensors. In this study, the collected input and output data employed in the St-Id system are acquired by series of vision-based measurements. The following novelties are proposed, developed and demonstrated in this project: a) vehicle load (input) modeling using computer vision, b) bridge response (output) using full non-contact approach using video/image processing, c) image-based structural identification using input-output measurements and new damage indicators. The input (loading) data due vehicles such as vehicle weights and vehicle locations on the bridges, are estimated by employing computer vision algorithms (detection, classification, and localization of objects) based on the video images of vehicles. Meanwhile, the output data as structural displacements are also obtained by defining and tracking image key-points of measurement locations. Subsequently, the input and output data sets are analyzed to construct novel types of damage indicators, named Unit Influence Surface (UIS). Finally, the new damage detection and localization framework is introduced that does not require a network of sensors, but much less number of sensors. The main research significance is the first time development of algorithms that transform the measured video images into a form that is highly damage-sensitive/change-sensitive for bridge assessment within the context of Structural Identification with input and output characterization. The study exploits the unique attributes of computer vision systems, where the signal is continuous in space. This requires new adaptations and transformations that can handle computer vision data/signals for structural engineering applications. This research will significantly advance current sensor-based structural health monitoring with computer-vision techniques, leading to practical applications for damage detection of complex structures with a novel approach. By using computer vision algorithms and cameras as special sensors for structural health monitoring, this study proposes an advance approach in bridge monitoring through which certain type of data that could not be collected by conventional sensors such as vehicle loads and location, can be obtained practically and accurately

Policy Response, Social Media and Science Journalism for the Sustainability of the Public Health System Amid the COVID-19 Outbreak: The Vietnam Lessons

Vietnam, with a geographical proximity and a high volume of trade with China, was the first country to record an outbreak of the new Coronavirus disease (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 or SARS-CoV-2. While the country was expected to have a high risk of transmission, as of April 4, 2020—in comparison to attempts to contain the disease around the world—responses from Vietnam are being seen as prompt and effective in protecting the interests of its citizens, with 239 confirmed cases and no fatalities. This study analyzes the situation in terms of Vietnam’s policy response, social media and science journalism. A self-made web crawl engine was used to scan and collect official media news related to COVID-19 between the beginning of January and April 4, yielding a comprehensive dataset of 14,952 news items. The findings shed light on how Vietnam—despite being under-resourced—has demonstrated political readiness to combat the emerging pandemic since the earliest days. Timely communication on any developments of the outbreak from the government and the media, combined with up-to-date research on the new virus by the Vietnamese science community, have altogether provided reliable sources of information. By emphasizing the need for immediate and genuine cooperation between government, civil society and private individuals, the case study offers valuable lessons for other nations concerning not only the concurrent fight against the COVID-19 pandemic but also the overall responses to a public health crisis

Experiment and FEM Modelling of Bond Behaviors between Pre-stressing Strands and Ultra–High–Performance Concrete

The objective of this paper is to investigate the bond properties of prestressing strands embedded in Ultra–High–Performance Concrete (UHPC).The UHPC was made in laboratory using local materials in Vietnam.Its mixture contains: silica aggregates, portland cement PC40, fly ash, silica fume, polycarboxylate superplasticizer and the micro steel fibers.The experimental process is realized on a pull-out test. The volume fraction of micro steel fibers in UHPC was 2%. The prestressing strand with diameters of 15.2mm was considered. The interface shear strength between strand and UHPC is identified based on the results of force and displacement obtained during the pull-out test. The Cohesive Zone Model (CZM) is implemented in finite element model to study this interface behavior. This model described by a piecewise linear elastic law. The CZM’s parameters are identified based on experimental results of pull-out test.The numerical studies are used the CZM in ANSYS software. Two numerical tests are realized and compared with experimental results: pull-out test and other test to verify the deflection of I girder due to prestressing force

Experiment and FEM Modelling of Bond Behaviors between Pre-stressing Strands and Ultra–High–Performance Concrete

The objective of this paper is to investigate the bond properties of prestressing strands embedded in Ultra–High–Performance Concrete (UHPC).The UHPC was made in laboratory using local materials in Vietnam.Its mixture contains: silica aggregates, portland cement PC40, fly ash, silica fume, polycarboxylate superplasticizer and the micro steel fibers.The experimental process is realized on a pull-out test. The volume fraction of micro steel fibers in UHPC was 2%. The prestressing strand with diameters of 15.2mm was considered. The interface shear strength between strand and UHPC is identified based on the results of force and displacement obtained during the pull-out test. The Cohesive Zone Model (CZM) is implemented in finite element model to study this interface behavior. This model described by a piecewise linear elastic law. The CZM’s parameters are identified based on experimental results of pull-out test.The numerical studies are used the CZM in ANSYS software. Two numerical tests are realized and compared with experimental results: pull-out test and other test to verify the deflection of I girder due to prestressing force

Computer Vision-Based Displacement And Vibration Monitoring Without Using Physical Target On Structures

Although vision-based methods for displacement and vibration monitoring have been used in civil engineering for more than a decade, most of these techniques require physical targets attached to the structures. This requirement makes computer vision-based monitoring for real-life structures cumbersome due to need to access certain critical locations. In this study, a non-target computer vision-based method for displacement and vibration measurement is proposed by exploring a new type of virtual markers instead of physical targets. The key points of measurement positions obtained using a robust computer vision technique named scale-invariant feature transform show a potential ability to take the place of classical targets. To calculate the converting ratio between pixel-based displacement and engineering unit (millimetre), a practical camera calibration method is developed to convert pixel-based displacements to engineering unit since a calibration standard (a target) is not available. Methods and approaches to handle challenges such as low contrast, changing illumination and outliers in matching key points are also presented. The proposed method is verified and demonstrated on the UCF four-span bridge model and on a real-life structure, with excellent results for both static and dynamic behaviour of the two structures. Finally, the method requires a simple, less complicated and more cost-effective hardware compared to conventional displacement and vibration monitoring measuring technologies

Completely Contactless Structural Health Monitoring Of Real-Life Structures Using Cameras And Computer Vision

A newly developed, completely contactless structural health monitoring system framework based on the use of regular cameras and computer vision techniques is introduced for obtaining displacements and vibrations of structures, which are critical responses for performance-based design and evaluation of structures. To provide contactless and practical monitoring, the current vision-based displacement measurement methods are improved by eliminating the physical target attachment. This is achieved by means of utilizing imaging key-points as virtual targets. As a result, pixel-based displacements of a monitored structural location are determined by using an improved detection and match key-points algorithm, in which false matches are identified and discarded almost completely. To transform pixel-based displacements to engineering units, a practical camera calibration method is developed because calibration standard on a physical target no longer exists. Moreover, a framework for evaluating the accuracy of vision-based displacement measurements is established for the first time, which, in return, provides users with the most crucial information of a measurement. The proposed framework along with a conventional sensor network and a data acquisition system are applied and verified on a real-life stadium during football games for structural assessment. The results obtained by the new method are successfully validated with the data acquired from sensors such as linear variable differential transformers and accelerometers. Because the proposed method does not require any type of sensor and target attachment, common field works such as sensor installation, wiring, maintaining conventional data acquisition systems are not required. This advantage enables an inexpensive and practical way for structural assessment, especially for real-life structures. Copyright © 2016 John Wiley & Sons, Ltd

Structural Identification Using Computer Vision-Based Bridge Health Monitoring

This paper presents a new structural identification (St-Id) framework along with a damage indicator, displacement unit influence surface using computer vision-based measurements for bridge health monitoring. Unit influence surface (UIS) of a certain response (e.g., displacement, strain) at a measurement location on a beam-type or plate-type structure (e.g., single-span or multispan bridge with its deck) is defined as a response function of the unit load with respect to the any given location of the unit load on that structure. The novel aspect of this paper is a framework integrating vehicle load (input) modeling using computer vision and the development of a new damage indicator, UIS, using image-based structural identification. This framework is demonstrated on the large-scale bridge model in the University of Central Florida Structures Laboratory for verification and validation. The UIS damage indicators successfully identified the simulated damage on the bridge model, including damage detection and damage localization

Hybrid Sensor-Camera Monitoring For Damage Detection: Case Study Of A Real Bridge

This 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

Camera-based Bridge Safety Monitoring

14th East Asia-Pacific Conference on Structural Engineering & Construction (EASEC-14), Ho Chi Minh City, Vietnam, 6-8 January 2016This paper describes a research project focused on the safety assessment of bridges using camera-based technologies. It is a collaboration with partners in three countries: Ireland, the United Kingdom and the United States. A major challenge of the project is the development of algorithms and methods that transform the measured sensor signals and video images into a form that is highly damage-sensitive/ change-sensitive for bridge safety assessment. The study will exploit the unique attributes of computer vision systems, where the signal is "continuous in space". This research will significantly advance current sensor-based structural health monitoring with computer-vision techniques, leading to practical applications for damage detection of complex structures with a novel approach. In the long term, monitoring with cameras is expected to be more broadly utilized for structural engineering purposes because of its potential for inexpensive deployment in real life bridges. While advancing the knowledge by integrating multidisciplinary concepts from theory to application, this research will have direct benefits as civil infrastructure (and particularly aged bridges) has become a critical societal concern from safety and cost perspectives. The paper will describe the bridge monitoring system that will be developed. It will include a weigh-in-motion (WIM) system to weigh vehicles, with cameras to monitor both the traffic and the bridge. The WIM system and the 1st camera will track the traffic and will extract its properties. The 2nd camera with some supplementary sensors will monitor the response of the bridge to the traffic. Structural identification algorithms will transform all of this data into damage indicators that indicate when the bridge has deteriorated or changed. The system will be tested using numerical simulation, scale models in the laboratory and trials using full scale bridges in the field.Science Foundation IrelandNational Science FoundationDepartment for Employment and Learning Northern IrelandInvest Northern Irelan

Locating and quantifying damage in beam-like structures using modal flexibility-based deflection changes

This paper presents an enhanced method to locate and quantify damage in beam-like structures using changes in deflections estimated from modal flexibility (MF) matrices. The method is developed from explicit relationship between a series of MF-based deflection change vectors and the damage characteristics. Based on this, three damage locating criteria are defined and used to detect and locate damage. Once the damage is located, its severity is estimated conveniently from a closed-form function. The capability of the proposed method is examined through numerical and experimental verifications on a steel beam model. The result shows that the method accurately locates and quantifies damage under various scenarios using a few modes of vibration, with satisfactory or even better results compared to those obtained from traditional static deflection-based method. The performance of the proposed method is also compared with three well-known vibration-based damage detection methods using changes in MF and modal strain energy. It is found that the proposed method outperformed the other three methods, especially for multiple damage cases. As beams can represent various structural components, the proposed method provides a promising damage identification tool targeting the application to real-life structures. </p