Intelligent Steel Bridge Coating Assessment Using Neuro‐Fuzzy Recognition Approach

Chen, Po‐Han; Chang, Luh‐Maan

doi:10.1111/1467-8667.00278

Cited by 17 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both methods start with the same step of converting the original image to a grayscale image. After the conversion to grayscale, where the grayscale is expressed using light intensities from 0 to 255 (0 is black, and 255 is white), one of two recognition methods are applied: (1) Neuro-Fuzzy Recognition Approach (NFRA) [88,90];…”

Section: Digital Image Processing Methodsmentioning

confidence: 99%

Non-Destructive Testing Applications for Steel Bridges

et al. 2021

View full text Add to dashboard Cite

The growing population and increasing demand for surface transportation have highlighted the importance of maintaining safe and reliable civil infrastructures for daily use. Among all civil infrastructures, bridges are one of the most important elements in the transportation system. As such, to prevent any failures caused by aging and environmental impacts, bridges require periodic inspections. This becomes even more critical due to climate change and its effect on bridges, especially in the coastal regions. Most of the inspections conducted incorporate the visual type of evaluation due to its simplicity. However, with the current developments in new technologies, there is a need for more advanced techniques of structural health monitoring (SHM) methods to be incorporated in the maintenance programs for more accurate and efficient surveys. In this paper, non-destructive testing (NDT) methods applicable to steel bridges are reviewed, with a focus on methods applicable to local damage detection. Moreover, the methodology, advantages and disadvantages, and up-to-date research on NDT methods are presented. Furthermore, the application of novel NDT techniques using innovative sensors, drones, and robots for the rapid and efficient assessment of damages on small and large scales is emphasized. This study is deemed necessary as it compiles in one place the available information regarding NDT methods for in-service steel bridges. Access to such information is critical for researchers who intend to work on new or improved NDT techniques.

show abstract

Section: Digital Image Processing Methodsmentioning

confidence: 99%

Non-Destructive Testing Applications for Steel Bridges

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Most of the studies focus on overcoming the problem. Chen and Chang 96 proposed the neuro-fuzzy recognition approach for rust detection and solved the non-uniform illumination problem. In this approach, the threshold value for image binarization and segmentation is generated from a pre-trained neural network.…”

Section: Rust Detection In Steel Structuresmentioning

confidence: 99%

“…Neuro-fuzzy recognition: Chen and Chang, 96 Lee et al 94 k-means: Lee et al, 94 Liao and Lee 91 Multivariate discriminant functions: Lee et al 97 SVM: Chen et al 93 Decision tree: Kim et al 101 ANN: Shen et al 92…”

Section: Machine Learningmentioning

confidence: 99%

A review of computer vision–based structural health monitoring at local and global levels

Dong

Çatbaş

2020

Structural Health Monitoring

418

165

View full text Add to dashboard Cite

Structural health monitoring at local and global levels using computer vision technologies has gained much attention in the structural health monitoring community in research and practice. Due to the computer vision technology application advantages such as non-contact, long distance, rapid, low cost and labor, and low interference to the daily operation of structures, it is promising to consider computer vision–structural health monitoring as a complement to the conventional structural health monitoring. This article presents a general overview of the concepts, approaches, and real-life practice of computer vision–structural health monitoring along with some relevant literature that is rapidly accumulating. The computer vision–structural health monitoring covered in this article at local level includes applications such as crack, spalling, delamination, rust, and loose bolt detection. At the global level, applications include displacement measurement, structural behavior analysis, vibration serviceability, modal identification, model updating, damage detection, cable force monitoring, load factor estimation, and structural identification using input–output information. The current research studies and applications of computer vision–structural health monitoring mainly focus on the implementation and integration of two-dimensional computer vision techniques to solve structural health monitoring problems and the projective geometry methods implemented are utilized to convert the three-dimensional problems into two-dimensional problems. This review mainly puts emphasis on two-dimensional computer vision–structural health monitoring applications. Subsequently, a brief review of representative developments of three-dimensional computer vision in the area of civil engineering is presented along with the challenges and opportunities of two-dimensional and three-dimensional computer vision–structural health monitoring. Finally, the article presents a forward look to the future of computer vision–structural health monitoring.

show abstract

“…Not only image processing techniques applied in food research, but also its applications are industry, medicine and some other areas. In the fields of automation and computerization, the detection and assessment of physical and biological damage played an important role in the steel industry [83,84], hardwood grading [85], forestry [86] and oil production [87]. Dimensions of workpieces [88], volume measurement of citrus fruits [89], size measurement of cereal grain [90] based on image processing technology have been proposed.…”

Section: Image Processing Techniquesmentioning

confidence: 99%