جستجوی مقالات مرتبط با کلیدواژه « discrete wavelet transform » 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.

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.