Crack identification method of highway tunnel based on image processing

In this paper,the images of tunnel surface are obtained by tunnel lining rapid inspection system,and tunnel crack forest dataset(TCFD)is established.The disaster characteristics of tunnel cracks are analyzed and summarized.Solutions of tunnel crack segmentation(TCS)method are developed for the detection and recognition of cracks on tunnel lining.According to the image features of the tunnel lining and the optical principal of detection equipment,effective image pre-processing steps are carried out before crack extraction.The tunnel image of TCFD is divided into appropriate number of blocks to magnify the local features of tunnel cracks.Local threshold segmentation method is used to traverse the blocks successively,and the first target block with crack is obtained.The seed in the target block were obtained by adaptive localization method and mapped to the whole image.Region growing is performed through crack seed until complete tunnel crack is extracted.The results show that the precision,recall rate and F-measure of tunnel cracks under the TCS method can reach 92.58%,93.07%and 92.82%without strong interference.According to the binary images processed by TCS method,the projection images of different types of tunnel cracks and their respective laws are obtained.Furthermore,the TCS method is implemented and deployed as a GUI software application.

Crack identification in functionally graded material framed structures using stationary wavelet transform and neural network

In this paper,an integrated procedure is proposed to identify cracks in a portal framed structure made of functionally graded material(FGM)using stationary wavelet transform(SWT)and neural network(NN).Material properties of the structure vary along the thickness of beam elements by the power law of volumn distribution.Cracks are assumed to be open and are modeled by double massless springs with stiffness calculated from their depth.The dynamic stiffness method(DSM)is developed to calculate the mode shapes of a cracked frame structure based on shape functions obtained as a general solution of vibration in multiple cracked FGM Timoshenko beams.The SWT of mode shapes is examined for localization of potential cracks in the frame structure and utilized as the input data of NN for crack depth identification.The integrated procedure proposed is shown to be very effective for accurately assessing crack locations and depths in FGM structures,even with noisy measured mode shapes and a limited amount of measured data.

Crack detection using integrated signals from dynamic responses of girder bridges

An innovative approach for the identification of cracks from the dynamic responses of girder bridges was proposed.One of the key steps of the approach was to transform the dynamical responses into the equivalent static quantities by integrating the excitation and response signals over time.A sliding-window least-squares curve fitting technique was then utilized to fit a cubic curve for a short segment of the girder.The moment coefficient of the cubic curve can be used to detect the locations of multiple cracks along a girder bridge.To validate the proposed method,prismatic girder bridges with multiple cracks of various depths were analyzed.Sensitivity analysis was conducted on various effects of crack depth,moving window width,noise level,bridge discretization,and load condition.Numerical results demonstrate that the proposed method can accurately detect cracks in a simply-supported or continuous girder bridges,the five-point equally weighted algorithm is recommended for practical applications,the spacing of two discernable cracks is equal to the window length,and the identified results are insensitive to noise due to integration of the initial data.

Identification of crack in a structural member using improved radial basis function(IRBF)neural networks

Purpose–The purpose of this paper is to provide an effective and simple technique to structural damage identification,particularly to identify a crack in a structure.Artificial neural networks approach is an alternative to identify the extent and location of the damage over the classical methods.Radial basis function(RBF)networks are good at function mapping and generalization ability among the various neural network approaches.RBF neural networks are chosen for the present study of crack identification.Design/methodology/approach–Analyzing the vibration response of a structure is an effective way to monitor its health and even to detect the damage.A novel two-stage improved radial basis function(IRBF)neural network methodology with conventional RBF in the first stage and a reduced search space moving technique in the second stage is proposed to identify the crack in a cantilever beam structure in the frequency domain.Latin hypercube sampling(LHS)technique is used in both stages to sample the frequency modal patterns to train the proposed network.Study is also conducted with and without addition of 5%white noise to the input patterns to simulate the experimental errors.Findings–The results show a significant improvement in identifying the location and magnitude of a crack by the proposed IRBF method,in comparison with conventional RBF method and other classical methods.In case of crack location in a beam,the average identification error over 12 test cases was 0.69 per cent by IRBF network compared to 4.88 per cent by conventional RBF.Similar improvements are reported when compared to hybrid CPN BPN networks.It also requires much less computational effort as compared to other hybrid neural network approaches and classical methods.Originality/value–The proposed novel IRBF crack identification technique is unique in originality and not reported elsewhere.It can identify the crack location and crack depth with very good accuracy,less computational effort and ease of implementation.

A BIEM OPTIMIZATION METHOD FOR FRACTURE DYNAMICS INVERSE PROBLEM

In the present paper, based on the theory of dynamic boundary integral equation, an optimization method for crack identification is set up in the Laplace frequency space, where the direct problem is solved by the author's new type boundary integral equations and a method for choosing the high sensitive frequency region is proposed. The results show that the method proposed is successful in using the information of boundary elastic wave and overcoming the ill-posed difficulties on solution, and helpful to improve the identification precision.