Abstract
As the frequency of earthquakes has increased in Korea in recent years, designing earthquake-resistant facilities has been increasingly emphasized. Structures constructed with rebars are vulnerable to shaking, which reduces their seismic performance and may result in damage to human life and property. Because the construction of facilities requires the maintenance of sub-constructions, such as by cutting rebars or compensating for missing rebars, information on rebar diameter is required. In this study, the YOLO-v3 algorithm, which has the fastest object recognition performance, was applied to the structural correction data, and a basic experiment was conducted in the air to predict the diameter of rebars in a facility, in real time based on ground-penetrating radar data. The reason for using the YOLO-v3 algorithm is that in the case of GPR data that change slightly according to the diameter of the reinforcing bar, it is difficult to discriminate with the naked eye, and the result may change depending on the inspector. The model achieved a higher accuracy than conventional rebar detection and diameter prediction methods. In addition, the possibility of real-time rebar diameter prediction during construction, using the proposed method, was verified.
Highlights
Westudied studiedaarebar rebardiameter diameterestimation estimationtechnique techniquefor forquickly quicklyestimating estimatingrebar rebardiamdiameter eterand andobtaining obtainingextensive extensiveinformation informationininthe thefield, field,using usingGPR
ground-penetrating radar (GPR) image in advance, an experiment conducted obtainaccurate accuraterebar rebar diameter prediction results using a convolutional neural network (CNN), which is widely used for image classification based on GPR image data
Acrylic box specimens and rebar specimens were prepared for GPR data acquisition, and B-scan and migrated data were obtained by varying the diameter and arrangement of the rebars
Summary
The frequency of earthquakes in Korea has increased, and the possibility of damage to existing buried objects in the process of facility redevelopment and maintenance is increasing [1]. It is necessary to obtain information on buried objects not shown in drawings, as well as objects at a given location that can be confirmed from existing drawings. Because it is necessary to acquire information for the execution of subsidiary work such as cutting rebars or compensating for missing rebars, a method for inspecting rebars to supplement information, such as that offered by facility drawings, is required. Examples of reinforcement work conducted as a result of missing rebars or the cutting of rebars include work conducted to address the missing rebars at the Mokgam Water Quality Restoration Center in 2013 [2], the missing rebars at the Daewoo
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