Deep learning-based automated image segmentation for concrete petrographic analysis

Shen, Yu; Huang, Zilong; Shen, Chuanyue; Shi, Humphrey; Lange, David A.

doi:10.1016/j.cemconres.2020.106118

Cited by 68 publications

(24 citation statements)

References 44 publications

(61 reference statements)

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“…These methods can be helpful for examining individual pixels and their nearby segments to determine portions of an image that have strong contrast. The method of identifying edges in image processing is known as ED [20]. Image segmentation is a necessary step in image analysis.…”

Section: Review Of Related Literatures 21 Image-processing Analysismentioning

confidence: 99%

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Kim

Choi

et al. 2021

Sensors

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With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure’s existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly. This paper presents a multivariate analysis approach for images, specifically for assessing substantial damage (such as cracks). The image analysis provides graph representations that are related to the image, such as the histogram. In addition, image-processing techniques such as grayscale are also implemented, which enhance the object’s information present in the image. In addition, this study uses image segmentation and a neural network, for transforming an image to analyze it more easily and as a classifier, respectively. Initially, each concrete structure image is preprocessed to highlight the crack. A neural network is used to calculate and categorize the visual characteristics of each region, and it shows an accuracy for classification of 98%. Experimental results show that thermal image extraction yields better histogram and cumulative distribution function features. The system can promote the development of various thermal image applications, such as nonphysical visual recognition and fault detection analysis.

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Section: Review Of Related Literatures 21 Image-processing Analysismentioning

confidence: 99%

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Kim

Choi

et al. 2021

Sensors

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“…The process of measuring the content of old mortar using the image method was as follows: First, the recycled concrete was polished by using a water-milling machine (5000 r/min, 900 W) and then scanned using a scanner (Canon CanoScan LiDE 700F, 4800 × 4800 dpi, Ho Chi Minh City, Vietnam) layer by layer to obtain the distribution map of RCAs at different depths. Then the deep learning method was used to identify and process the scanned graphics in order to obtain a clearer distribution map of RCA and mortar [ 28 ]. The manufacturing process is shown in Figure 5 .…”

Section: Experimental Programmentioning

confidence: 99%

Influence of Parent Concrete Properties on Compressive Strength and Chloride Diffusion Coefficient of Concrete with Strengthened Recycled Aggregates

et al. 2020

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Parent concrete coming from a wide range of sources can result in considerable differences in the properties of recycled coarse aggregate (RCA). In this study, the RCAs were obtained by crushing the parent concrete with water-to-cement ratios (W/Cparent) of 0.4, 0.5 and 0.6, respectively, and were strengthened by carbonation and nano-silica slurry wrapping methods. It was found that when W/Cparen was 0.3, 0.4 and 0.5, respectively, compared with the mortar in the untreated RCA, the capillary porosity of the mortar in the carbonated RCA decreased by 19%, 16% and 30%, respectively; the compressive strength of concrete containing the carbonated RCA increased by 13%, 11% and 13%, respectively; the chloride diffusion coefficient of RAC (DRAC) containing the nano-SiO2 slurry-treated RCA decreased by 17%, 16% and 11%; and that of RAC containing the carbonated RCA decreased by 21%, 25% and 26%, respectively. Regardless of being strengthened or not, both DRAC and porosity of old mortar in RCAs increased with increasing W/Cparent. For different types of RCAs, DRAC increased obviously with increasing water absorption of RCA. Finally, a theoretical model of DRAC considering the water absorption of RCA was established and verified by experiments, which can be used to predict the DRAC under the influence of different factors, especially the water absorption of RCA.

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“…For the computation of the various 3D parameters, a convolutional neural network (CNN) model was used. More details about the CNN model can be found in literature [21]. This model was developed based on the state-of-theart CNN DeepLabv3 [22] and ResNet-101 [23] and has shown remarkable performance [24,25].…”

Section: D Analysismentioning

confidence: 99%

Influence of Pumping/Extrusion on the Air-Void System of 3D Printed Concrete

et al. 2020

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3D printing with concrete offers many advantages over conventional construction methods and interest in this field of research has seen a rapid rise in recent years. While there have been large number of cases of successful fabrication of 3D printed structures, ensuring the long-term durability performance of the fabricated structures is equally important. Freeze-thaw damage is one such deterioration issue, especially in cold places like Switzerland. This study investigated the effect of the different processing conditions encountered in 3D printing, namely pumping, acceleration/mixing and extrusion on the air void system of a standard 3D printable mortar mix. The 3D void size distribution of the air voids obtained using a recently developed stereological model, along with the ASTM C457 results showed that pumping had the major impact on the void structure as both air content and spacing factor decreased significantly. The effect of acceleration/change in rheology and extrusion on the void structure was also prominent from the obtained results. The 3D protected paste volume (PPV) curves showed that pumping and acceleration processes could enhance the freeze-thaw performance.

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Deep learning-based automated image segmentation for concrete petrographic analysis

Cited by 68 publications

References 44 publications

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Multivariate Analysis of Concrete Image Using Thermography and Edge Detection

Influence of Parent Concrete Properties on Compressive Strength and Chloride Diffusion Coefficient of Concrete with Strengthened Recycled Aggregates

Influence of Pumping/Extrusion on the Air-Void System of 3D Printed Concrete

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