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Investigation and Detection of Cracks for Health Monitoring of Concrete Structures Using Computer Vision

Shojaei, Masoud | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56193 (45)
  4. University: Sharif University of Technology
  5. Department: Aerospace Engineering
  6. Advisor(s): Adibnazari, Saeed
  7. Abstract:
  8. Structural Health Monitoring (SHM) of civil infrastructures is of paramount importance in ensuring the safety and reliability of these structures. SHM involves the use of sensors and data analysis techniques to continuously monitor the structural condition of infrastructure, detect damage or degradation, and provide insights for maintenance and repair. Concrete cracks are one of the most common and critical types of damage in civil infrastructure, which can compromise the structural integrity and safety of the infrastructure if left undetected and untreated. Therefore, the development of effective and efficient crack detection techniques using computer vision and machine learning can significantly contribute to SHM and enhance the safety and longevity of civil infrastructures. This master thesis proposes a novel approach for concrete crack detection using computer vision techniques and Convolutional Neural Networks (CNNs). The study utilized a dataset of crack and uncracked images, and developed a CNN model to accurately classify these images. Furthermore, the study deployed the model on Roboflow, a cloud-based machine learning platform, which allows for crack detection in images and videos in real-time, directly from a web browser. The use of Structural Health Monitoring (SHM) and Artificial Intelligence (AI) is at the forefront of this research, and the proposed model is written in Python programming language. Overall, the thesis presents a promising solution for detecting concrete cracks using computer vision and machine learning techniques, which can contribute to the development of more effective and efficient maintenance strategies for civil infrastructure
  9. Keywords:
  10. Crack Detection ; Machine Vision ; Images Classification ; Neural Network ; Structural Health Monitoring ; Real-Time Detection ; Convolutional Neural Network ; Concrete Crack Detection

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