In the present work, an image processing technique that automatically detects and analyses cracks in the digital image of concrete surfaces is proposed. The image processing technique automates the measurement of crack characteristics including the width, length, orientation and crack pattern. In the proposed technique, a morphological technique was applied to correct the non-uniform brightness of the background, and enhanced binarisation and shape analysis were used to improve the detection performance; furthermore, detailed algorithms to calculate the crack width, length, orientation and an artificial neural network to recognise crack patterns including horizontal, vertical, diagonal (−45°), diagonal (+45°), and random cracks are proposed. An image processing program was developed for the proposed algorithm and a series of experimental and analytical investigations were performed to assess the validity of the algorithm. Then, the crack characteristics measured using the proposed technique were compared with those obtained using a conventional technique. The test results showed that the crack characteristics can be accurately measured and analysed using the proposed technique.

Water is often added to concrete placing for easy workability and finishability in construction site. The additional mixing water can help easy mixing and workability but causes increased porosity, which yields degradation of durability and structural performances. In this paper, cement mortar samples with 0.45 of W/C (water to cement) ratio are prepared for control case and durability performances are evaluated with additional water from 0.45 to 0.60 of W/C. Several durability tests including strength, chloride diffusion, air permeability, saturation, and moisture diffusion are performed, and they are analyzed with changed porosity. The changing ratios and patterns of durability performance are evaluated considering pore size distribution, total porosity, and additional water content.