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Several previous studies have attempted to optimize the crack recognition process for concrete values. For example, a computer vision system for a train inspection monorail was proposed and installed in the Large Hadron Collider to gather data from various sensors and capture images by the European Organization for Nuclear Research, only purposed for recording data and reducing personnel intervention (Attard et al., 2018). The recognition process of engineering concrete cracks has been automated to a certain extent based on deep learning methods (Chheng & Likitlersuang, 2018), including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Transfer Learning (2017b; Cha et al., 2018; Huang et al., 2018; Xue & Li, 2018; Zhang et al., 2017a). Overall, the structural crack repair process is slow, labor intensive, and subjective so far. To overcome these working limitations, automatic repair systems have to be developed (Kovačević et al. 2021).
A decision support system for crack repair is developed, which includes a crack feature database and a crack-repair method knowledge base. Table 1 lists some of the keywords used in the crack feature database. The data requirements imposed by some recent researches are summarized into 10 categories and 7 aspects. The characteristics of the crack feature database are installed in 8 major ports, used to describe the crack characteristics. Text Type (2) consists of the length and depth, components, location, material, property, width, and direction of cracks, defined as Enumeration Type (6). The number is in Int Type (1). Each data picture is listed in the table using Blob Type (1). Before the image processing, the pixel resolution and image capture should be done. After preprocessing and enhancement of grayscale images and others images, they can capture the pictures of cracks, as shown in Fig. 7. The threshold method of segmentation is used after smoothening the images' spatial filtering. And the length and width of cracks are also calculated to evaluate the parameters of images of cracks by analogy. The data are collated in Excel to perform detection and high-performance transformation, and then converted to a record for the crack feature database. Table 4 shows the attribute table structure of the crack features (containing 200 entries of data). The crack feature data for every interface are transformed into the database, as shown in Fig. 8.
The database framework of the knowledge base is established, as presented in Table 2. The repair methods' data requirements can be divided into 10 types and 8 aspects. The characteristics of the repair methods are installed in 6 major interfaces. Text Type (7) is composed of technology number, name, range, material, tool, process, and reference. Steps and robotic execution code are defined as Blob Type (2). The number is Int Type (1). Table 5 shows the attribute table structure of the crack repair methods. The repair technology data for each port are converted to the established database. The Excel data table converted for the database is shown in Fig. 9, which now consists of six types of process data, which will continue to expand in the future.
The semi-autonomous robotic platform is implemented in a laboratory, which includes industrial robot integrating multifunctional tools, workbench, and repairing components, as shown in Fig. 12. In the course of experiment, each step has its code to command the robot, including: sealant extrusion and smearing (Fig. 12a), grout nipple grabbing (Fig. 12b), grout nipple pasting (Fig. 12c), cleaning and injection (Fig. 12d), plug grabbing (Fig. 12e), and plug installation and curing (Fig. 12f). Functional fixture for grabbing the grout nipple and plug is driven by a MHL2-40D cylinder. Both the motion path and speed of each operation of the robot are controlled through the control procedures, so as to make the pose and the grabbing speed of the construction adjustable. In addition, the coordinates of these moving points that have been calculated before will be compared with those acquired from the process simulation of the concrete crack repair process. The data are utilized when the teaching apparatus is deployed to control the accuracy of the process. And all the operations are implemented in the ABB programming language, as elaborated below. It is important to control the accuracy during the construction process, because the accuracy can even affect the quality of the component repairing results.
Repair process of cracked concrete: a sealant extrusion and smearing; b grout nipple grabbing; c grout nipple pasting; d cleaning and injection; e plug grabbing; and f plug installation and curing
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