Earticle

초록

영어

The main visual feature is surface cracks, which are caused by loopholes that are embedded into the structures due to manufacturing faults and overloading factors. The structural health monitoring must be precise and more efficient for detecting surface cracks in concrete structures. Human inspection is used to identify damage on concrete surfaces. But, these traditional visual observation techniques are not more effective for large concrete structures. Moreover, this outmoded human labor practice for crack detection is intensive, expensive, and inefficient. To predict potentially hazardous situations caused by cracks on concrete surfaces, it is crucial to have an efficient, fast, and well-organized inspection system for concrete surface cracks. The automatic crack detection system must be effective in identifying cracks, damage, and segmenting them. In this research, a deep learning (DL) algorithm model is active for crack detection in concrete structure images to assess the influence on structural health. This design work is projected to provide a fast and active solution for identifying dust/duct type to prevent power losses using an image classification model based on DL. The VGG-16 DL model significantly analyzes the precision and accuracy of identifying the crack surfaces on concrete structures. The exceptional performance of the projected model achieves a training accuracy of 99.99% and a test accuracy of 99.96%, with an F1 score of 0.9995, precision of 0.9995, and a sensitivity of 0.9997. This is more precise, costeffective, and more efficient than human resources to find the defect on concrete surfaces that may support the healthy life of concrete structures.

목차

Abstract
I. INTRODUCTION
II. LITRATURE REVIEW
III. MATERIALS AND METHODS
A. Methodology
B. Dataset
C. VGG-16
IV. SIMULATION AND RESULTS
A. Simulation
B. Simulation
C. Results
V. CONCLUSION AND FUTURE WORK
REFERENCES

저자

• Asghar Ali Shah [ Department of Computer Science Kateb University Kabul, 1007, Afghanistan ]
• Abdul Rafay [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
• Naila Sammar Naz [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
• Muhammad Salik [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
• Fahad Ahmed [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
• Shahzada Atif Naveed [ Department of Computer Science Rashid Latif Khan University, Lahore, 54000, Pakistan. ]

참고문헌