This paper presents the detections of the subsurface features and distresses in roadways and bridge decks from ground penetrating radar (GPR) data collected at traffic speed. This GPR system is operated at 2 GHz with a penetration depth of 60 cm in common road materials. The system can collect 1000 traces a second, has a large dynamic range and compact packaging. Using a four channel GPR array, dense spatial coverage can be achieved in both longitudinal and transversal directions. The GPR data contains significant information about subsurface features and distresses resulting from dielectric difference, such as distinguishing new and old asphalt, identification of the asphalt-reinforced concrete (RC) interface, and detection of rebar in bridge decks. For roadways, the new and old asphalt layers are distinguished from the dielectric and thickness discontinuities. The results are complemented by surface images of the roads taken by a video camera. For bridge decks, the asphalt-RC interface is automatically detected by a cross correlation and Hilbert transform algorithms, and the layer properties (e.g., dielectric constant and thickness) can be identified. Moreover, the rebar hyperbolas can be visualized from the GPR B-scan images. In addition, the reflection amplitude from steel rebar can be extracted. It is possible to estimate the rebar corrosion level in concrete from the distribution of the rebar reflection amplitudes.
This paper develops an automatic method to identify pavement layer properties from ground penetrating radar (GPR) data collected at traffic speed. The GPR system is operated at a center frequency of 2 GHz with a penetration depth of 60 cm in common road materials. Features include the capability of collecting up to 1000 traces/s, a large dynamic range, and compacted packaging. Using a four-channel GPR system, a large amount of data are collected at traffic speed on urban roads for over 200 lane miles, providing a dense spatial coverage. The GPR data contain information about the pavement layer properties, including layer interface, dielectric constant, and layer thickness. Using cross correlation and Hilbert transform algorithms, the pavement layer properties are identified from the large GPR data sets automatically and efficiently. The method has been successfully demonstrated in engineering applications for the accurate estimation of the layer thickness with excellent repeatability. Moreover, thickness data from different radar channels at the same location are used for transversal profile prediction. By searching abnormal variations of layer properties and amplitude of reflection signals, features and/or possible distresses in surface and subsurface, such as full-depth asphalt patches and prepothole conditions, can be detected.
The scarcity of the proper tool for fast developing intelligent toy is the one of problem that affects developing the top grade toys of our country. This paper presents a method that is based on the tree structure for developing intelligent toys, which is supported by a friendly man-machine interface, is able to realize restructuring and modularizing design of the program, debugging and controlling intelligent toy smartly, and to avoid some mistakes of a program. This method with the help of Problem Analysis Diagram (PAD) transforms a programming task of the intelligent toy into the tree structure, translates and executes every node of the tree structure. The corresponding commands and data can be transmitted to the controller of an intelligent toy, also be generated into a downloaded program for the chip microprocessor.