Determination of Geometric Parameters of Cracks in Concrete by Image Processing

Yuriy Vashpanov,Gwanghee Heo,Tatyana Podousova,Jung-Young Son,Yong Suk Kim

doi:10.1155/2019/2398124

Abstract

The 8-bit RGB image of a cracked concrete surface, obtained with a high-resolution camera based on a close-distance photographing and using an optical microscope, is used to estimate the geometrical parameters of the crack. The parameters such as the crack’s width, depth, and morphology can be determined by the pixel intensity distribution of the image. For the estimation, the image is transformed into 16-bit gray scale to enhance the geometrical parameters of the crack and then a mathematical relationship relating the intensity distribution with the depth and width is derived based on the enhanced image. This relationship enables to estimate the width and depth with ±10% and ±15% accuracy, respectively, for the crack samples used for the experiments. It is expected that the accuracy can be further improved if the 8-bit RGB image is synthesized by the images of the cracks obtained with different illumination directions.

Highlights

Cracks in concretes are one of the important parameters in diagnosing the current status of structures [1,2,3]
Since each pixel in the image has a corresponding crack or concrete surface point, the light amount coming into each pixel is related to the point distance from the illuminating light source and the camera because the light intensity is reduced in proportional to the distance square
The 2nd setup consists of a binocular microscope (5) for magnifying the crack image, a Raspberry Pi camera v2 (3) installed at one eye position of the microscope for recording the image, and the halogen lamp (4), the same as in the 1st setup

Summary

Introduction

Cracks in concretes are one of the important parameters in diagnosing the current status of structures [1,2,3]. Since each pixel in the image has a corresponding crack or concrete surface point, the light amount coming into each pixel is related to the point distance from the illuminating light source and the camera because the light intensity is reduced in proportional to the distance square. Measuring the pixel intensity distribution of the crack image will provide simultaneously the crack width, depth, and shape information In this case, the camera needs to have a high resolution and be located at a close distance from the crack to have more pixels on its portion for the better accuracy. The pixel intensity distribution of crack images that are obtained with a high-resolution camera at a close distance to the crack is used to estimate the crack’s width, depth, and shape. The distribution allows estimating the crack’s depth and shape size that cannot be possible with the typical camera images of cracks

Experimental Setup for Obtaining Images of Cracks in Samples

Imaging Processing with OriginLab for

Visualization of the Cracks in Buildings in

Computer Synthesis of Images of Complex Cracks

Findings

Conclusion