Computer Vision Based Inspection Approach to Predict Damage State and Load Level for RC Members

Davoudi, Rouzbeh; Miller, Gregory R.; Kutz, J. Nathan

doi:10.12783/shm2017/14225

Cited by 11 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Training set x i ; Label set y i = H ; Initialize: a i = 0, f i = À b; Compute: L, H using equations (11) and (12); Update: a 1 , a 2 using equations (13) and (14); Repeat: Update f 1 , f 2 using equation (15); Compute L 1 , H 1 using equation (15); Update a 1 , a 2 using equations (13) and (14); Until: f 1 converge to f 2 ; Update: b 1 , b 2 using equations (16) and (17). Store new a 1 and a 2 .…”

Section: Results Discussionmentioning

confidence: 99%

“…5 As the structure becomes more complicated, the research on the advanced solutions to the crack inspection and detection has attracted significant attention. [5][6][7] Consequently, an increasingly number of structural health monitoring (SHM) applications with noncontact means have been developed and applied for the monitoring and inspection of cracks in a variety of critical concrete structures, 8,9 such as plates, 10 columns, 11,12 beams, 13 tubes, 14,15 bridges, [16][17][18][19][20] dam, 21 wind turbine, 22,23 and others. Compared to the traditional contact methods, the majority of noncontact SHM methods have improvement in the convenience and efficiency of structural inspection and crack detection.…”

Section: Introductionmentioning

confidence: 99%

“…19,28 Computer vision technology applies multidisciplinary technologies-which include optical imaging, image processing, artificial intelligent, and machine learning-to derive meaningful information and gain high-level understanding from images or videos. 36 In the last two decades, computer vision has been increasingly utilized in SHM 17,18,26,33,37 which expedites inspection process, decreases costs and safety risk, increases accuracy, and facilitates the access of structure. 28 Unquestionably, computer vision-based SHM (CVSHM) has been a state-of-the-art research area.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of a new low-cost unmanned aerial vehicle and vision-based concrete crack inspection method

Lei

Ren

Wang

et al. 2020

Structural Health Monitoring

View full text Add to dashboard Cite

With the explosive development of the computer vision technology, more and more vision-based inspection methods enabled by unmanned aerial vehicle technologies have been researched on the crack inspection of the sundry concrete structures. However, because of the limitation of the low-cost unmanned aerial vehicle hardware, whose cost is around US$500, most of the vision-based methods are difficult to be implemented on the low-cost unmanned aerial vehicle for real-time crack inspection. To address this challenge, in this article, a new computationally efficient vision-based crack inspection method is designed and successfully implemented on a low-cost unmanned aerial vehicle. Furthermore, to reduce the acquired data samples, a new algorithm entitled crack central point method is designed to extract the effective information from the pre-processed images. The proposed vision-based crack detection method includes the following three major components: (1) the image pre-processing algorithm, (2) crack central point method, and (3) the support vector machine model–based classifier. To demonstrate the effectiveness of the new inspection method, a concrete structure inspection experiment is implemented. The experimental results indicate that this new method is able to accurately and rapidly inspect the cracks of concrete structure in real time. This new vision-based crack inspection method shows great promise for the practical application.

show abstract

Section: Results Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of a new low-cost unmanned aerial vehicle and vision-based concrete crack inspection method

Lei

Ren

Wang

et al. 2020

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…In addition to crack detection, crack quantification is also important for assessing the status of in-service infrastructure and determining corresponding maintenance measures. Further, many researches attempted to correlate detailed crack patterns to the quantitative damage states of concrete beams and panels at different loading stages [42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques

Miao

Srimahachota

2021

Construction and Building Materials

View full text Add to dashboard Cite

“…A wall-climbing robot was developed for monitoring reinforced concrete and detect corrosion to perform inspection using impact-echo and metric learning support vector machine (SVM). 15 In the last two decades, computer-vision based methods are largely utilized in structural health monitoring (SHM) [16][17][18][19] of large scale concrete structures to facilitate automation and expedite the inspection process. Consequently, a progression of SHM applications is seen in a wide variety of concrete structures, such as dams, 20 road tunnels, 21 and wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network

Saleem

Park

Lee

et al. 2020

Structural Health Monitoring

View full text Add to dashboard Cite

The structural condition of bridges is generally assessed using manual visual inspection. However, this approach consumes labor, time, and capital, and produces subjective results. Therefore, industries today are using automated visual inspection approaches, which quantify and localize damages such as cracks using robots and computer vision. This paper proposes an instant damage identification and localization approach that uses an image capturing and geo-tagging system and deep convolutional neural network for crack detection. The image capturing and geo-tagging allows the geo-tagging of three-dimensional coordinates and camera pose data with bridge inspection images; the deep convolutional neural network is trained for automated crack identification. The damages extracted by the convolutional neural network are instantly transformed into a global bridge damage map, with georeferencing data acquired using the image capturing and geo-tagging. This method is experimentally validated through a lab-scale test on a wall and a field test on a bridge to demonstrate the performance of the instant damage map.

show abstract

Computer Vision Based Inspection Approach to Predict Damage State and Load Level for RC Members

Cited by 11 publications

References 0 publications

Design of a new low-cost unmanned aerial vehicle and vision-based concrete crack inspection method

Design of a new low-cost unmanned aerial vehicle and vision-based concrete crack inspection method

Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques

Instant bridge visual inspection using an unmanned aerial vehicle by image capturing and geo-tagging system and deep convolutional neural network

Contact Info

Product

Resources

About