Monitoring and analysis of crack developments in concrete using machine vision - Kapadia, Harsh K.; Patel, Paresh V.; Patel, Jignesh B. (Journal of Structural Engineering, 2024)
Periodic inspection of reinforced concrete bridges and buildings is required to assess their deterioration due to loadingand environmental factors. Monitoring the condition of the existing structure helps in the assessment of its loadcarryingcapacity. Cracking in concrete structure is one of the critical parameters representing its structural health.Trained personnel monitor the development of cracks and their progression at the critical locations of the structuresthrough a physical vision at regular intervals of time. With the advancement in computational techniques, machinevision is becoming a robust alternative to physical inspection of the structure and its health monitoring. The presentwork demonstrates the application of machine vision in concrete crack monitoring by identifying the location of thecrack, the number of cracks, the length of the cracks, and the area of the cracks. A novel system has been developedby integrating machine vision and convolutional neural networks to gain real-time images of concrete surfaces, detectconcrete cracks, and extract various parameters related to cracks, such as number, location, length, and area, insynchronization with the applied loading. The present system is implemented for real-time crack monitoring duringcompression testing of concrete cubes of size 150 mm × 150 mm × 150 mm of different characteristic strengths. Theoutcome of the machine vision system in graphical form is presented for various parameters of cracks like the number,location, length, the area concerning compressive load for concrete of different strengths. An accuracy of 98% has beenachieved for crack detection on concrete cubes as presented in the results. The present machine vision system can beimplemented on different concrete structures for acquiring real-time data on crack development and progression. Theproposed framework will be an effective tool for engineers working in the domain of structural health monitoring ofconcrete structures.