The aim of this work is to obtain estimates of the accuracy of the x-ray limited-angle tomography method for determining the shape and size of internal cracks in large-sized reinforced concrete structures. A fan-beam registration scheme with a source moving along a circular arc is considered. For reconstruction, we use the fan-beam transform inversion formula and the algebraic method ART. By means of numerical simulation, the dependences of errors of the first and second kind on the mutual orientation of the central beam and the direction of the crack, the angular size of the source trajectory and the noise level are obtained.
The aim of the paper is to improve the accuracy of the estimation of the shape and size of cracks in the welded seams of pipelines from their X-ray tomograms. Based on the results of previous studies, the procedure of deconvolution of projection data before reconstruction is considered. The influence of the value of the regularization parameter in the deconvolution algorithm based on the Fourier transform on the quality of the tomograms is investigated. In the numerical simulations, the estimates of the optimal values of the regularization parameter for different data recording conditions are derived.
The problem of automatic determination of the location of defect projections on radiographs of metal and reinforced concrete products is considered. The main research method is a computational experiment. A three-dimensional phantom has been developed that simulates a fragment of a concrete slab reinforced with iron bars with defects inside. Its X-ray image is modeled on the basis of the Bouguer law. Distortion represents uncorrelated noise with a normal distribution in each pixel. A binary Bayesian classifier is used to search for defects. It has been shown to be quite effective as long as the noise SDV does not exceed 1.5% of the average image brightness. At a higher noise level, the classifier does not give stable results. The use of simple low-frequency filtering methods (averaging and median in a sliding window) for noise suppression did not lead to improvement. However, the use of the entropy filter has shown that it can improve the quality of classification. Special image point detectors, in particular the Harris-Stephans detector, were also used to search for defects. The results obtained suggest that this approach may be promising.
The previously proposed method of increasing the visibility of cracks on tomograms obtained from X-ray sensing of sections of metal elements of building structures has been developed. A multiscale decomposition of tomogram lines is performed using the Haar wavelet basis. The detailed decomposition coefficients corresponding to the level of the assumed crack width are multiplied by a constant, the value of which is determined based on the noise level and the difference between the amplitude of the crack image and the surrounding background. In contrast to the previous work, where these parameters were assumed to be set, here we suggest the methods for its evaluating from the measurements. In particular, a simple formula is obtained that relates the variance of uncorrelated noise in the projection data to that of noise in the tomogram. The method is implemented as a computational algorithm based on which a computer program is developed. Numerical modeling was performed. The errors of the first and second kind were used toevaluate the effectiveness of the method determining whether a pixel belongs to a crack image. Binary classification was used for their calculation. Dependences of errors on the number of projections and the noise level on them were obtained. The results of the simulations showed that the use of the proposed method can reduce errors by 1.7-2.5 times.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.