Automated Steel Bridge Coating Inspection using Neural Network Trained Image Processing

Elbeheri, Ahmed; Zayed, Tarek

doi:10.1201/9781315189390-344

Cited by 2 publications

(2 citation statements)

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“…Despite the subjective and error-prone nature of these manual inspection procedures [2][3][4][5][6], this type of inspection is still the common process in most construction projects. However, in the last few decades, there have been attempts to utilize advancing tools and technologies for automating the inspection processes in construction projects.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Automated Construction Inspection and Progress Monitoring (ACIPM): Applications, Challenges, and Future Directions

Samsami

2024

CivilEng

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Despite the subjective and error-prone nature of manual visual inspection procedures, this type of inspection is still a common process in most construction projects. However, Automated Construction Inspection and Progress Monitoring (ACIPM) has the potential to improve inspection processes. The objective of this paper is to examine the applications, challenges, and future directions of ACIPM in a systematic review. It explores various application areas of ACIPM in two domains of (a) transportation construction inspection, and (b) building construction inspection. The review identifies key ACIPM tools and techniques including Laser Scanning (LS), Uncrewed Aerial Systems (UAS), Robots, Radio Frequency Identification (RFID), Augmented Reality (AR), Virtual Reality (VR), Computer Vision (CV), Deep Learning, and Building Information Modeling (BIM). It also explores the challenges in implementing ACIPM, including limited generalization, data quality and validity, data integration, and real-time considerations. Studying legal implications and ethical and social impacts are among the future directions in ACIPM that are pinpointed in this paper. As the main contribution, this paper provides a comprehensive understanding of ACIPM for academic researchers and industry professionals.

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Section: Introductionmentioning

confidence: 99%

A Systematic Review of Automated Construction Inspection and Progress Monitoring (ACIPM): Applications, Challenges, and Future Directions

Samsami

2024

CivilEng

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“…At present, among the most common methods used to evaluate the state of the rust layer are the visual inspection method, the ultrasonic thickness measurement method, and the adhesive tape test method. Among the non-destructive testing methods for the monitoring of corrosion are the eddy current, ultrasonic inspection, acoustic emission, vibration analysis, radiography, thermography, and visual inspection [7,8]. However, to speed up detection efficiency, visual inspection is often used in the actual management and maintenance operation, and rust layer evaluation is carried out by comparing it with standard corrosion morphology photographs [9].…”

Section: Introductionmentioning

confidence: 99%

Corrosion grade recognition for weathering steel plate based on a convolutional neural network

Wang¹,

Shen²,

Wu³

et al. 2022

Meas. Sci. Technol.

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For the maintenance of weathering steel structure facilities, it is necessary to evaluate the corrosion grade of the rust layer on the surface regularly. At present, the corrosion grade classification of weathering steel is mainly based on the human-eye inspection. In this paper, a deep learning method using a convolutional neural network for evaluating the corrosion grade of weathering steel is proposed to save time and manpower. Firstly, the image dataset of the corrosion steel plate was established using salt spray tests. Then, a CNN architecture named VGG-Corrosion was designed to evaluate the corrosion grade of the corroded steel plate. The effect of the learning rate, transfer learning, and batch size was also investigated to clarify the best hyperparameter configurations to train a powerful corrosion grade classification model. Under the best combination of considered hyperparameters, the mean average accuracy for the corrosion grade evaluation of the test results is 90.96%. The testing results indicated that the CNN based corrosion grade recognition for weath-ering steel plate is prospective, which would be helpful for safety evaluation of steel structures.

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Automated Steel Bridge Coating Inspection using Neural Network Trained Image Processing

Cited by 2 publications

References 0 publications

A Systematic Review of Automated Construction Inspection and Progress Monitoring (ACIPM): Applications, Challenges, and Future Directions

A Systematic Review of Automated Construction Inspection and Progress Monitoring (ACIPM): Applications, Challenges, and Future Directions

Corrosion grade recognition for weathering steel plate based on a convolutional neural network

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