In this paper, a fully automatic algorithm to detect rebar in reinforced concrete from Ground Penetrating Radar (GPR) data is developed and validated. GPR is a widely used electromagnetic test method in bridge engineering, which is efficient, non-destructive, and sensitive to the dielectric difference. The exact rebar locations can be known from the surveyed GPR data of bridge decks manually, which is time-consuming. Therefore, an automatic, accurate and robust algorithm is necessary to engineering applications. The automatic algorithm proposed in the paper is a pattern recognition method. First, a template of a rebar hyperbola in a GRP image is selected; second, a rebar is detected from the local minimum points in the map of Sum of Square Difference (SSD); third, an output file is created and saved, including the geographical location of each detect rebar. Finally, the proposed method is validated from field tests in a reinforced concrete bridge, and the detection accuracy is 91.1%, which is very promising.
KEYWORDS: Inspection, 3D image processing, Stereoscopic cameras, Data acquisition, Roads, 3D acquisition, System integration, Sensors, Imaging systems, Geographic information systems
This paper develops an automatic system for the surface distress inspections on the urban public areas, including sidewalks, squares, and parking lots. This inspection system integrates an industrial 3D camera, a positioning system supporting both GNSS and Beidou, a distance measurement instrument (DMI), an inertial measurement unit (IMU), a power supply, a time synchronization technology, and a data acquisition software, such that it can collect 3D images of the surfaces of the urban public areas at a normal walking speed (~5km/hr). This inspection system has several advantages over the traditional manual inspections, such as low cost, high efficiency, low power consumption and compacted size. The field tests are conducted on the urban public areas in Nan’an District to validate the effectiveness of the inspection system. The engineering applications indicate that the inspection system works well and collects high-quality 3D images in either time mode or distance mode. The collected 3D images will be used for further analysis of the surface distress extractions, such as crack, pothole, and faulting. Finally, a basic neutral network is created to determine if there are distresses in the 3D images from field tests with the accuracy higher than 90%, which is very promising.
Roadway stack plays an indispensable role in the whole cycle of road exploration design, construction and maintenance management. However, due to human factors, the settlement error of road 100-meter piles between the actual station and the designed station is unavoidable, which has significant influences on the subsequent road maintenance management. In this paper, in order to calibrate this type of error, a calibration strategy and the Geographic Information System (GIS) secondary development is realized, which combines ArcGIS model builder with Python script. Finally, an automatic calibration tool is developed. The automatic batching processing method for the roadway stack calibration improves the accuracy and efficiency of the vehicle-based pavement maintenance.
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