Advances in Measuring Air-Void Parameters in Hardened Concrete Using a Flatbed Scanner

Abstract

Entrained air bubbles in concrete improve the freeze–thaw resistance by alleviating stresses caused by freezing water within the microstructure. It is necessary to achieve a uniform distribution of entrained air bubbles in the paste to achieve optimal freeze–thaw performance. The technique of measuring the air-void system parameters in hardened concrete is specified by ASTM C457/C457M-12. In this standard testing procedure, the freeze–thaw parameters of interest are determined by optically observing and measuring the traversal lengths of air voids, paste, and aggregate particles along straight lines across the polished surface of the concrete specimen. ASTM C457/C457M-12 has been criticized as time-consuming and expensive, and so there has been a concerted effort by the research community to develop improved methods. Automated methods that use digital images to measure freeze–thaw parameters exist, but they require operator-selected threshold levels and significant sample preparation time and labor. To address these shortcomings, this paper presents an automated method to measure freeze–thaw parameters using a high-resolution flatbed scanner, automated image segmentation, and short sample preparation time and labor. Polished concrete surfaces are treated with phenolphthalein and a florescent chalk powder to distinguish phases of interest. The surfaces are scanned using a flatbed scanner, and spectral–spatial analysis is used to identify the individual phases of aggregates, paste, and air voids. The ternary images are used to measure parameters of the air-void system. To validate the rigor of this automated method, six groups of concrete specimens provided by a commercial laboratory were tested using the proposed automated method and manual ASTM C457/C457M-12. This comparison was conducted in a “blind” fashion, so as to avoid potential biases. A fair agreement is found for normal concrete specimens, but not for concrete containing lightweight aggregates.

Image-based restoration of the concrete void system using 2D-to-3D unfolding technique

“…The method requires the operator to inspect the polished concrete surface under a stereomicroscope using either the linear traverse or point count approach [8]. In recent years, the conventional C457 test has been further automated with image analysis techniques using digital scans of polished concrete, implementing Powers' approach to determine spacing factor [9][10][11][12][13][14][15]. Meanwhile, computed tomography (CT) has made it possible to probe the actual concrete void size distribution; however, this test does not allow evaluation of a representative volume, especially for concrete, and the equipment is typically inaccessible to most practitioners [16,17].…”

Development of the Measuring Techniques for Estimating the Air Void System Parameters in Concrete Using 2D Analysis Method

“…None of those methods have been adopted for general use. Methods of determining the Philleo factor from 2D measurements were proposed by Załocha and Kasperkiewicz [9], Wawrzeńczyk and Kozak [10], and by Song et al [11]. Parameters of the air void system have been determined through fractal analysis [12,13] and recently, directly in 3D space, using X-ray computed tomography [14][15][16][17][18].…”

“…Song et al [11] used an approach called spectral analysis which was adopted to segment each scanned RGB image. Polished concrete surfaces were treated with phenolphthalein and a florescent chalk powder to distinguish between the phases.…”

“…Considering this fact, the internal voids in the aggregate have to be masked before the examination by painting the aggregate with a permanent ink pen under a stereomicroscope. A typical surface preparation is to ink the surface black, fill the voids with a white powder, remove the excess from the surface, and finally, ink out any filled defects [25].Automated image analysis can be performed in two ways: (a) analysis of many single images of small size or (b) analysis of one large image created after stitching many small images or after scanning the large surface of a concrete sample [11,26,27].In the first approach, the specimen is analysed using a large number of small, one-size fields of measurement. The field boundaries on the specimen can truncate a significant number of features whose measured or apparent size will be smaller than their true size, leading to underestimations.…”

“…Automated image analysis can be performed in two ways: (a) analysis of many single images of small size or (b) analysis of one large image created after stitching many small images or after scanning the large surface of a concrete sample [11,26,27].…”