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

Zaurín, Ricardo; Khuc, Tung; Çatbaş, F. Necati

doi:10.1061/(asce)be.1943-5592.0000811

Cited by 39 publications

(22 citation statements)

References 16 publications

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“…Yeum and Dyke proposed a vision‐based automated crack detection method for bridge; object detection and grouping techniques are utilized to extract images of possible damage regions; its performance is successfully validated using images collected by a handheld camera from a large‐scale rusted steel beam with cracks. Zauri et al proposed a monitoring system based on the combination of video images and conventional sensor network data to identify the possible damage on a movable steel beam bridge in Florida, USA; images and senor data are utilized to extract a series of unit influence lines, and the statistical outlier‐detection algorithm is combined to detect and localize common scenarios on the real‐world bridge successfully. Valenca et al proposed an automated crack detection method for concrete bridges based on image processing and laser scanning; terrestrial laser scanning technology is utilized to capture the geometric information of the bridge, which is used to correct images collected from the bridge; the method is validated by the experiment on a concrete viaduct at IC2 road, in Rio Maior, Portugal.…”

Section: Recent Progress On Damage Identification Methods For Beam Brmentioning

confidence: 99%

Recent progress and future trends on damage identification methods for bridge structures

Chatzi

Sim

et al. 2019

Struct Control Health Monit

231

112

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Summary Damage identification forms a key objective in structural health monitoring. Several state‐of‐the‐art review papers regarding progress in this field up to 2011 have been published. This paper summarizes the recent progress between 2011 and 2017 in the area of damage identification methods for bridge structures. This paper is organized based on the classification of bridge infrastructure in terms of fundamental structural systems, namely, beam bridges, truss bridges, arch bridges, cable‐stayed bridges, and suspension bridges. The overview includes theoretical developments, enhanced simulation attempts, laboratory‐scale implementations, full‐scale validation, and the summary for each type of bridges. Based on the offered review, some challenges, suggestions, and future trends in damage identification are proposed. The work can be served as a basis for both academics and practitioners, who seek to implement damage identification methods in next‐generation structural health monitoring systems.

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Section: Recent Progress On Damage Identification Methods For Beam Brmentioning

confidence: 99%

Recent progress and future trends on damage identification methods for bridge structures

Chatzi

Sim

et al. 2019

Struct Control Health Monit

231

112

View full text Add to dashboard Cite

show abstract

“…In addition to detection of local defects of structures, there are also studies on identifying global damages of the structures. Zaurin et al (2015) performed motion tracking algorithms to measure the mid-span deflections of bridges under the live traffic load (23). Computer Vision is also used to process ground penetration radar (GPR) and infrared thermography (IRT) images that are useful to identify delamination formed inside the concrete structures.…”

Section: Overview Of Deep Learning Approaches In Damage Detection Andmentioning

confidence: 99%

Artificial Intelligence Assisted Infrastructure Assessment using Mixed Reality Systems

Karaaslan

Bağcı

Çatbaş

2019

Transportation Research Record: Journal of the Transportation R

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Word count: 5,240 word texts + 3 tables x 250 words (each) = 5,990 words Submission Date: 08/01/2018 ABSTRACTConventional methods for visual assessment of civil infrastructures have certain limitations, such as subjectivity of the collected data, long inspection time, and high cost of labor. Although some new technologies (i.e. robotic techniques) that are currently in practice can collect objective, quantified data, the inspector's own expertise is still critical in many instances since these technologies are not designed to work interactively with human inspector. This study aims to create a smart, human-centered method that offers significant contributions to infrastructure inspection, maintenance, management practice, and safety for the bridge owners. By developing a smart Mixed Reality (MR) framework, which can be integrated into a wearable holographic headset device, a bridge inspector, for example, can automatically analyze a certain defect such as a crack that he or she sees on an element, display its dimension information in real-time along with the condition state. Such systems can potentially decrease the time and cost of infrastructure inspections by accelerating essential tasks of the inspector such as defect measurement, condition assessment and data processing to management systems. The human centered artificial intelligence (AI) will help the inspector collect more quantified and objective data while incorporating inspector's professional judgement. This study explains in detail the described system and related methodologies of implementing attention guided semi-supervised deep learning into mixed reality technology, which interacts with the human inspector during assessment. Thereby, the inspector and the AI will collaborate/communicate for improved visual inspection.

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“…50 This method operates under the assumption that neighboring pixels have similar motion. With this assumption, it is possible to stack many of these equations into one system, as in equation (9), for a neighborhood of n pixels…”

Section: Feature Extractionmentioning

confidence: 99%

Marker-free monitoring of the grandstand structures and modal identification using computer vision methods

Dong

Celik

Çatbaş

2018

Structural Health Monitoring

100

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In this study, a vision-based multi-point structural dynamic monitoring framework is proposed. This framework aims to solve issues in current vision-based structural health monitoring. Limitations are due to manual markers, single-point monitoring, and synchronization between a multiple-camera setup and a sensor network. The proposed method addresses the first issue using virtual markers—features extracted from an image—instead of physical manual markers. The virtual markers can be selected according to each scenario, which makes them versatile. The framework also overcomes the issue of single-point monitoring by utilizing an advanced visual tracking algorithm based on optical flow, allowing multi-point displacement measurements. Besides, a synchronization mechanism between a multiple-camera setup and a sensor network is built. The proposed method is first tested on a grandstand simulator located in the laboratory. The experiment is to verify the performance of displacement measurement of the proposed method and conduct structural identification of the grandstand through multi-point displacement records. The results from the proposed method are then compared to the data gathered by traditional displacement sensors and accelerometers. A second experiment is conducted at a stadium during a football game to validate the feasibility of field application and the operational modal identification of the stadium under human crowd jumping through the measured displacement records. From these experiments, it is concluded that the proposed method can be employed to identify modal parameters for structural health monitoring.

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

Cited by 39 publications

References 16 publications

Recent progress and future trends on damage identification methods for bridge structures

Recent progress and future trends on damage identification methods for bridge structures

Artificial Intelligence Assisted Infrastructure Assessment using Mixed Reality Systems

Marker-free monitoring of the grandstand structures and modal identification using computer vision methods

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