Telecharger [PORTABLE] Crack Wave Xtractor
Telecharger Crack Wave Xtractor
A problem with the existing detection algorithms is that they are static. This makes them unsuitable for performing real-time monitoring of structures. The idea is to simulate an elastic solid and to use it to demonstrate that cracks can be predicted by monitoring the propagation of waves in this solid. This can be accomplished by a meso-scale model which tracks the propagation of waves and the stresses which result from these waves. The meso-scale model can also be used to provide a baseline reference or to calculate a baseline stress field. Another problem that can be modeled with this approach is the use of the meso-scale model as an internal crack guide for the entire structure.
There are very few papers available in the literature that deal with crack detection in concrete structures. In one of these papers, the cracks are predicted by observing the dynamic response of acoustic transducers. They are placed on specific locations in the concrete and the signals from these transducers are amplified by an array of microphones and the amplitudes of the signals are compared with each other to determine the presence of cracks in the concrete.
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At present, crackle noise measurement approaches based on a DSP (Digital Signal Processor) have been widely explored, and a growing number of hardware-related solution has been proposed. The researchers recognize that the DSP resources have already reached that limit and explore hardware solutions based on FPGA (Field Programmable Gate Array). In this paper, an FPGA-based crackle noise measurement scheme is proposed. The key components of the system are an embedded spectrum analyzer, an FPGA-based front-end circuit, a crackle signal pre-processing unit and the FPGA architecture.
In this paper, a new idea based on local morphology in a scale space is developed to characterize cracks without any prior knowledge of their shapes. We use an automatic crack segmentation technique to delineate and extract cracks for the texture feature. This feature is composed of mean, entropy, inverse difference variance and the gray moment invariants, and the features are extracted from various scales with a cubic B-spline interpolation. Finally, we use a discriminative classifier to distinguish crack from non-crack regions. Crack classification results are evaluated using two hardware platforms: the FPGA platform and the commercial GPU platform, and the result shows that the proposed method can produce high quality cracks on both platforms.
As a new architecture of Deep Neural Networks, Convolution Neural Networks (CNNs) have shown potential for imaging. Initially designed to image objects on a 2D plane, CNNs can also be extended to 3D by adding channels and multiple layers. Especially the simple but powerful design of the CNN makes it possible to detect a crack directly from the scans without taking prior information of damage types.30,31. In this work, we extend the work in [30] to deal with more complex situations such as different damage types. We use a multi-views CNN that consists of 3D inputs (X, Y and Z directions of crack), 3D outputs (same directions) and 7 convolutional layers. We show that it can detect cracks on both synthetic and real cracks regardless of their shapes. The cracks can be detected only from inside, outside and top view without any prior information of damage types. Furthermore, we apply our method on a real crack data, where we are able to reconstruct a 3D representation of the crack, along with the crack length and its depths in different directions. When compared with [30], our method is more robust, and can deal with more complex crack types such as multiple cracks.
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