جستجوی مقالات مرتبط با کلیدواژه « تبدیل موجک گسسته » در نشریات گروه « عمران »

Bridges are important components of the road networks and are classified as high importance from the structural safety standpoint. Therefore, maintaining their stability and improving their performance are important. The issue becomes more important considering that most of the existing bridges in the national transportation system are old, which can lead to their failure when subjected to dynamic loads caused by earthquakes or other sources. One of the conventional methods for health monitoring of structures and detecting the damage state is structural signal processing in the time-frequency domain using wavelet transformation. Previous studies proved the efficiency of this method. In this study, to benefit from the capabilities of this approach, one of the Jinnah grade-separation bridges in Tehran with a segmental deck under different damage scenarios has been studied. For this purpose, the difference in the displacement response between the intact and damaged bridge deck under the effect of the considered load is used as the input signal of wavelet transformation. The results show that the proposed method can accurately identify the damage location using one-time sampling and a minimum number of sensors.

Identification of infrastructure damage in the early stages is one of the most fundamental requirements through the maintenance process. In order to identify damages in the process of Structural Health Monitoring, numerous methods have been developed, including Neural Network (NN) Damage Identification Techniques, Time Series Damage Identification Techniques, Frequency Response Damage Identification Techniques, Force Spectrum Density Damage Identification Techniques, and Wavelet Damage Identification Techniques. The conventional structural health monitoring procedure depends on a comparison of undamaged primary structural data to the affected structural data. Comparative algorithms to indicate the location of damage based on the structural data has a significant effect on the structural health monitoring method performance. Therefore, a damage detection algorithm is the most important step in identifying damage in the structural health monitoring procedure. In this paper, a damage identification algorithm based on the relative wavelet entropy method is proposed for the structure. Using the definition of the discrete wavelet transform with wavelet entropy form the base of the proposed algorithm for damage detection to support the method efficiency. The proposed method is capable of identifying several sites of damage and is used as an effective method compared to other methods of damage detection.

Inspection of the pavement distresses is one of the most prominent phases of pavement management process in regard with determining optimum pavement maintenance strategies. Over the past few decades, a considerable number of efforts have been carried out on developing automatic methods for objectively distress detection all of which rely on machine vision and image processing techniques. One of the most important assets comprising machine vision systems is the feature extraction process. In the past few years, multi-resolutional analysis approaches, namely wavelet transforms has provided a great tool for fast and accurate image texture representation. In the present study, after acquisition of six different types of asphalt pavement distresses under controlled condition, in order to identify and categorize them, four 2-D multi-resolution transforms including Haar discrete wavelet, Daubechies3 discrete wavelet, Coiflet1 discrete wavelet and dual-tree complex wavelet were utilized. After decomposition of the distress images by applying the aforementioned transforms, first-order statistical indices based on histogram and second-order statistics based on gray level co-occurrence matrix were employed, in order to describe the wavelet frequency sub-bands texture. The distress classification results based on minimum Mahalanobis distance classifier indicate that extracting second-order statistics from the sub-bands of the dual-tree complex wavelet and Haar discrete wavelet transforms, yielding classification accuracy of 99% and 95% respectively, outperform other feature extraction algorithms in distress recognition. Furthermore, statistical indices acquired from gray level co-occurrence matrix with average classification rate of 87%, obtained superior performance in distress images discrimination compared to histogram statistics.

Change detection is one of the most important processes in photogrammetry and remote sensing, in which occurred changes in a same geographical area are identified over time. Forests are one of the national assets of any country that has a vital role in climate change, groundwater formation and prevention of floods and soil erosion. Thus, an accurate change detection method should be exploited to monitor and maintain forest regions. In this paper, an efficient unsupervised change detection method is proposed for this purpose. Here, two bitemporal sattelite images, acquired at the forest areas of Unitted stasted and Australia are employed  to evaluate the proposed change detection method. In the first step of the proposed approach, discrete wavelet transform was used to generate an efficient change index by fusing of two difference images derived by NDVI and GNDVI vegetation indices. Anisotropic diffusion filtering was then applied to obtain robust change index in which noises was reduced while change regions was highlited. In the next step, the generated index was segmented into changed and unchanged classes using an improved k-means algorithm. Finally, improved MRF model initialed with the initial change map is employed to generate final change map. The proposed MRF model include two novel improvements in the main energy function, resulting in preserveing changed region details. The proposed improved MRF contained superiority of 0.49% and 0.61% compared to traditional MRF in datasets 1 and 2, respectively. The proposed MRF also outperformed Otsu, PCA-kmeans, GAFCM and GMMMRF methods, so that reduced the total error rate by an average of 0.93% and 5.31% in data sets 1 and 2, respectively. In general, the proposed method has a high capability for accurate identifying changes for vegetated areas.

Localized singularities caused by changes in the stiffness or mass of the damaged region cannot be simply visible through smooth mode shape curves. However, the wavelet transform of input signal can identify the location of defects by sudden changes in the spatial variation of transformed response. The aim of this study is to present a new method for damage detection in a squarely damaged plate with element of reduced thickness with damage ratio 3% and to decrease the possible random noises that are come from different sources such as numerical solutions or physical situation. In this approach, the value of each point of mode shape data is subtracted from or is added with its symmetric point with respect to the plate’s point of symmetry (point O). The obtained value is located at its first point, again. The result of this process is a symmetrical mode shape matrix of the damaged plate, which is utilized as the input signal function in wavelet analysis.The results demonstrated that using reconstructed modal data is superior to the original modal data and they provide a better crack indicator than the result of the DWT of the original mode shape data. The effects of crack location and sampling interval are examined. The simulated results show that this approach is more effective in damage detection of plate-like structures, as the method requires the knowledge of only the response of the damaged plate as a baseline for crack detection.

Evaluation of pavement performance is one of the most prominent assets in choosing the beneficial strategy for pavement management operations. In the past two decades, a considerable number of investigations have been carried out on developing automatic methods for distress rocognition all of which rely on the machine vision and image processing techniques. In the past few years multi-resolutional analysis methods, namely wavelet transform has provided a great tool for fast and accurate auto-detection of distresses. In the present study, a method has been proposed utilizing the wavelet transform method which can analyze the texture surface of pavement considering the longitudinal, transverse and diagonal textural structures as the key elements. In this paper, after performing the discrete wavelet transform and decomposing the image into frequency sub-bands using 4 different wavelet families, properties of sub-bands texture has been acquired (based on grey level co-occurrence matrix) and compared to the results acquired based on image texture analysis in spatial domain. Finally, the minimal Mahalanobis distance method was applied in order to categorize the acquired images into seven classes including alligator cracking, without distress, longitudinal cracking, transverse cracking, bleeding, patching and raveling. Based on the results of validation and evaluation of the classifiction performance it was observed that the distress image classification using image texture analysis in the transformation domain leads to the more accurate results in comparison to spatial domain. The mean accuracy of distress image classification in transformation domain is 67% while the accuracy rate in classification of distress data based on extraction of texture features in spatial domain is 49/76%. In case of transformation domain, although Daubechies 2 filter has a better sensitivity rate in discrimination of bleeding distress, in general, the Haar filter outperformed other utilized wavelets in recognition and classification of asphalt pavement surface distresses with 95% accuracy.