جستجوی مقالات مرتبط با کلیدواژه « wavelet transform » در نشریات گروه « عمران »

Regarding the possibility of lifetime reduction of the structure due to different factors, including cracking and phenomena, such as earthquakes, it is crucial to constantly monitor the structure so that the location and severity of the damage can be timely identified to correctly estimate the remaining lifetime of the structure. Wavelet transform is a robust tool for identifying local structural damages, by which the damage location is identified by observing a disturbance in the wavelet coefficients of the damaged structure, regardless of the main (undamaged) structure response. This paper addresses the application of wavelet transforms for identifying multiple cracks in steel moment resisting frames, which has received less attention in the literature. To this end, different crack modes were considered in the form of element removal for one-story and two-story frames, and the first four mode shapes of the damaged structure were derived using the ABAQUS. Finally, the derived mode shapes were subjected to continuous coif5 and sym2 wavelet transform, and the capability of the method in identifying cracks in structures accounting for the effect of mode order and damage location was evaluated. The results indicated that if the proper wavelet was selected, the proposed method would be able to identify the damage location based on the response signal using a simple, robust, and reliable way. The results also showed that the damage in the beam was identified with a higher accuracy than the column foot. It was also shown that the location of the damage in the mid-span of the beam can be identified with a higher accuracy than the damage near to the column abutments. Also, it was observed that higher modes could identify the damage location with a higher resolution.

In this paper, an innovative finite element updating method is presented based on the sensitivity of wavelet transform coefficients of correlations function structural parameters is proposed to identify the damage. The Quasi-linear sensitivity of the wavelet coefficients of the WTCF concerning the structural parameters is evaluated based on incomplete measured structural responses. This WTCF sensitivity is more sensitive to local structural changes than the wavelet transform function response sensitivity. The model is updated by the wavelet transform coefficients achieved in the frequency range in the vicinity of the resonances, in which damping and incomplete measurements have no significant effect on the results of the parameter estimation. The proposed algorithm is used to estimate the structural parameters of the frame model. By the solution of the sensitivity equation through Least-squares method, the finite element model of the structure is updated for estimation of the location and severity of structural damages, simultaneously. The proposed method was successfully applied to a 2D frame model using simulated data contaminated by measurement and modeling errors. The robustness of the method against modeling and mass measurement errors is investigated by adding random errors to the mass parameters of the frame model.

Numerous researchers focus on monitoring the health of structures to ensure safety and reduce maintenance costs. Beams and columns are the primary elements of structures in civil engineering, and designers expect beams and columns to be the last elements to experience damage. This paper identifies steel beam damage based on dynamic modal data. After a modal analysis was performed on the modeled beam using the ABAQUS finite element software, modal information was extracted, including the frequencies and shapes of healthy and damaged modes. Due to the presence of damage, differences in the frequency values of primary and secondary conditions were observed. In addition, modal assurance criteria (MAC) values below one were obtained, confirming the presence of damage. Using an analytical method based on wavelet analysis, MATLAB.R2021a processed healthy and damaged mode shape signals. In all modes, a comparison of the output signal diagrams of healthy and damaged modes revealed the difference in the damaged area, allowing the damage locations to be identified with an error of less than 2 percent using a simple examination.

Peridynamic theory with a new formulation in the equations of motion, replaces the partial differential equations with integral equations. Due to this capability, it is possible to model crack initiation in any direction without the need to consider crack-growth criteria. One of the main problems in peridynamic theory is its high computational efforts due to its dynamic nature. If the critical time step of the numerical integration is greater than the loading time step, it will increase the cost of calculations. In this paper, using wavelet transform, peridynamic problems under irregular and random impact loads are analyzed. The aim of this study is to increase the computational speed for these problems. The method presented in this paper is investigated on two material models, namely Prototype brittle material and micro-plastic material. In this regard, structures with linear and nonlinear behavior have been analyzed considering the effects of discontinuities (such as cracks) and without considering the effects of discontinuities. The selected structures include two beams. Each beam is subjected to two types of irregular impact loading. The beams are analyzed once with the main impact (wave) function and once with the approximate impact (waves) functions obtained using wavelet transform. Based on the results of linear and nonlinear analyses of this study, it can be judged that the presented method reduces the computational cost by 87% in perdynamic models with linear behavior. It also bring a 94% reduction in computational costs in predynamic models with nonlinear behavior.

With the development and growth of the population in big cities; The need to use public transportation facilities such as the metro is growing. Given the importance of the urban transportation network, it is very important to pay attention to the health control of underground structures against static and dynamic loads. As Iran is one of the seismic countries in the world, in recent years it has witnessed devastating earthquakes and has caused heavy damage. According to previous studies, underground structures are more safe against earthquakes. Because surface structures are connected to the ground only on the floor and lower surface. If the underground structures are in full conflict with the surrounding environment and are more resistant to earthquake loads. However, given that most subways are built on shallow urban soils, there have been reports of severe damage to these underground spaces from earthquake loads. For this reason, identifying damage and controlling the health of underground spaces, especially subways, is very important. In this research, due to the uncertainty in the ground parameters and also the lack of accurate forecast of the drilling route, 3DEC software (when dealing with rock layers) along with PLAXIS3D2020 software has been used for static and dynamic analysis of Mashhad Metro Line 2 tunnel. In addition, in order to control the health of the structure (Mashhad Metro Line 2), wavelet transform (WT) methods have been used. For this purpose, the received signal was called by dynamic analysis and using the wavelet conversion toolbox in MATLAB software, damage areas were identified in the model (around the concrete cover and side borders), which obviously can be identified using the maintenance system. Properly prevented the collapse of the underground structure in the identified areas.

In areas near active faults, ground motions usually show a large pulse in velocity time history. These near-field pulse-like ground motions increase the structural failure and cause extensive damage to structures. Due to the limited number of pulse-like ground motion recorded in a range of magnitude and distance, there is a small set of possible earthquake scenarios for seismic engineering in a given area, especially in areas near the fault. A new method has been proposed in this paper for simulating near-field pulse-like ground motions using instantaneous feature modeling. The proposed method is introduced based on the time-frequency process which is necessary to consider non-stationary characteristics of ground motion. In this method of simulation, wavelet-based Hilbert transform is used to extract the non-stationary characteristics of original pulse-like ground motion. The stationary wavelet transform is first applied to decompose the original ground motion into a series of subband frequency, and then the instantaneous features are obtained by applying Hilbert transform on each subband frequency. Instantaneous frequencies are modeled by Gaussian distribution and the instantaneous amplitude fitted by Gaussian curves. Approximation coefficients obtained from the last level of wavelet transform are also simulated using the Gaussian curves. Pulse-like ground motion will be simulated using Hilbert and Wavelet inverse transforms and the new generated data for the instantaneous characteristics and approximation coefficients. In this study, four pulse-like ground motions of Imperial Valley1979 and Northridge 1994 were used to verify the effectiveness of this simulation method. Simulated ground motions were compared with the recorded pulse-like ground motions and various criteria were used to confirm the effectiveness of the proposed method. Peak ground acceleration, response spectrums, and arias curves which are important parameters of ground motion were used in this paper to validate the proposed method. Simulation results not only captured the main characteristics of the recorded pulse-like ground motion, but also preserved the effects of the pulse as well.

Pavement inspection is an important part of pavement management systems because this part provides input and raw material information to the system. If the pavement situation has not been assessed or incorrectly assessed, it will not be possible to carry out optimum maintenance and repair operations. It can also cause higher maintenance costs and the risk of accidents. Pavement distress information is crucial data that should be collected and evaluated in the pavement inspection process. Accordingly, wide research has been conducted to develop more efficient systems for the evaluation of pavement distresses using new technologies. Bleeding is one of the asphalt pavement distresses, which directly affects the skid resistance and vehicle maneuverability. Based on the literature, pavement bleeding received the attention from the research community less than other pavement distress such as cracks, rutting, raveling, and potholes. This research attempts to develop an efficient system for the automatic evaluation of asphalt pavement bleeding. For this aim, the transfer learning method was applied to train a pre-trained convolutional neural network for bleeding detection. Also, various image processing techniques (wavelet transform analysis as the main technique) were used to segment bleeding regions in pavement images. Results indicated that the proposed system has good performance in bleeding detection and segmentation with 98% and 87%, respectively. Accordingly, this system can be applied as an efficient system for pavement bleeding evaluation.

Engineering structures experience different conditions during their life time that may result in damages in some structural elements under these conditions. Beams are considered as the main components of building structures, bridges, and the most important parts of the industrial machinery therefore, it is important to identify the various situations of occurred local damages. In this paper the steel beam was modeled by the plain-ends of support conditions in the healthy and damaged states in the finite element software of ABAQUS and the static analysis was performed by the influence of constant static load. The frequency analysis of the steel beam was conducted without the influence of loading. Changes of fitting curve coefficients of static displacements resulting from the polynomial regression and also changes in the frequency values of the different modes of healthy and damaged states confirm the damage of the beam. In order to detect the various failure situations, the difference of static displacements and also the difference of the interpolated modes of healthy and damaged states analyzed using the continuous and discrete wavelet transformations. The interpolated details resulted from analyzing the continuous and discrete wavelet transformations at the site of damages show the irregularities and perturbations in the wavelet coefficients, such that the relative minimums and maximums of jump in wavelet coefficients happened in all investigated states in the site of damages. Also, results show the wavelet coefficients sensitivity at the site of each damage independent of the wavelet coefficients sensitivity in other damaged sites with different intensities. Also, the occurrence place of minimums and maximums of wavelet coefficients coincide in a damaged situation with different intensities (with approximately zero error).

Marine structures are exposed to harsh sea environments. These structures may suffer physical damages such as collision, explosion, and chemical ones like corrosion during their exploitation. Diagnosis of damages and their repair in these important structures increase their service life. To find the damage to a structural system, it is necessary to consider its effects. According to the theory of structures, the static and dynamic responses of any structure are related to its stiffness. As a result, any sudden change in the stiffness is accompanied by a change in the static and dynamic responses of the structure; thus, it is possible to detect the probable damage in a structural system by changing its responses before and after the damage. Extreme importance of civil structures on the one hand and their expensive maintenance costs on the other hand have led researchers to strive to find more accurate and useful methods for detecting structural damage to their early stages of occurrence. In this regard, wavelet transform, which is a powerful mathematical tool for signal processing, has attracted the attention of many researchers in the field of health monitoring. In this study, based on laboratory and numerical modeling, the damage detection process in the piles of a dolphin wharf was evaluated using wavelet energy that had high sensitivity to minor changes in a vibration signal. Also, without extracting the analytical equation of the damage index, the exact location of the damage is determined directly by calculating the continuous wavelet transform energy density (Scalogram). Evaluation of the results demonstrated that the proposed method in multiple damage simulation scenarios accurately predicted the location of three damages without any additional errors. While estimating the location of damages is accompanied by error in other methods. Comparison between the results obtained from the proposed method and laboratory results demonstrates the capability of the introduced method to detect multiple damages by calculating the continuous wavelet transform energy density.

A large number of structures experience minor damage in some elements during operation, therefore, the development of this type of damage reduces the service life of the structures and in some cases leads to total destruction. That's why, today, being assured of the health status of structures and their performance is proposed as one of the most important engineering issues. Considering the limitations of traditional damage detection methods, the methods based on changes in the vibrational characteristics of the structure have been formed. In this paper, given the modes shape of the panel prefabricated wall before and after destruction as a 2-D signal processing, the damaged zone was detected using discrete 2-D wavelet transform. At first, the panel prefabricated wall was modeled in the ABAQUS finite element software, and the frequency analysis was done to extract modal responses and was validated using modal responses of the corresponding laboratory sample. Changes in the values of natural frequencies, as well as the incompatibility of the modes shape based on the Modal Assurance Criterion (MAC) and the angle between the modes shape confirm the damage in the structure. Additionally, diagonal details from differential or summation wavelet transform analysis of the interpolated healthy and damaged modes shape (depending on the angle between the modes shape, if it approximately equals zero degree, the differential of modes shape is analyzed, and if it approximately equals 180 degree, the summation of modes shape is analyzed) indicate more irregularity of the wavelet coefficients in the damaged zone compared to other zones such that the jump relative maxima and minima in the wavelet coefficients have occurred in the damage occurrence zone.

Damage occurrence is always inevitable in structures. So far, many examples of damage types in engineering structures have been recorded with many losses of human and financial. For this reason, the detecting of structural damages during its exploitation to provide safety with the lowest cost has been the subject of many researchers in the last two decades. In this regard, the wavelet transform is a powerful mathematical tool for signal processing, has attracted the attention of many researchers in the field of health monitoring of structures. In this paper, due to the increase of steel plate shear wall in the building industry, it was considered the problem of detecting the location of the damage in steel plates. In this paper, due to the increase of steel plate shear wall in the building industry, it was considered the problem of detecting the location of the damage in steel plates. At first, the steel plate was modeled in ABAQUS finite element software with free support conditions, and then the healthy and damaged first eight mode shape was extracted. The primary and secondary modes shape was analyzed using discrete two-dimensional wavelet transform as a two-dimensional spatial signal. The results of the diagonal details of the wavelet analysis of secondary modes shape show the turbulence of the wavelet coefficients, compared with primary modes shape in damage locations; so that, wavelet analysis of the modes shape of the first mode, show damage location with the better equivalence of wavelet coefficients and the error of less than 6%.

Damage detection is a topic of great importance for structural health monitoring. Many varieties of structural damage can be detected by examining changes in structural response in terms of stiffness. Wavelet transform is a powerful mathematical tool for the processing and time-frequency analysis of transient signals and has great potential to be used in structural damage detection. In FRP-strengthened reinforced concrete and steel sections, stiffness changes can be caused by cracking, yielding of steel components, crushing of concrete, or rupture of FRP panels. With the help of wavelet transform, it is possible to use the continuous measurements of the response to bend or torsional loading to estimate the capacity of the cross section corresponding to the stiffness changes. In this paper, bending of FRP-reinforced steel beams filled by concrete under bending and CFRP-reinforced concrete beams under pure torsion is evaluated. The results show that the location of the damage appears as perturbations in the diagram of discrete wavelet coefficients, which indicate the time of cracking, yielding of steel, crushing of concrete in the compression zone, and rupture of FRP. Therefore, a wavelet transform-based data processing procedure can be used to estimate the cracking and yielding capacities of the beams subjected to torsion, the yielding capacity of the steel and the ultimate capacity of the beams subjected to bending. The results demonstrate a high level of agreement between the estimates obtained from the discrete wavelet transform method and the examined experimental and numerical data.

A designer needs to design a structure with the aim of obtaining the maximum possible load expected for the structure during its lifetime. In this paper, with regards to the information obtained from the earthquakes, the critical earthquakes were computed for a shear frame building, equipped with a belt truss system and subjected to two constraint scenarios. For this purpose, a nonlinear optimization problem has been solved, in which the objective function was the maximization of the roof displacement. In the first constraint scenario, the computed critical earthquake was known as the first state critical earthquake. In addition, for the second constraint scenario, the earthquake was named as the second state critical earthquake. In the first scenario, the energy and peak ground acceleration was considered as the constraints, while in the second scenario, the upper bound Fourier spectrum was added to these constraints. Finally, properties of the initial and critical earthquakes were investigated using the Fourier analysis method and continuous wavelet transform. The numerical results showed that the Fourier spectrum of the first critical earthquake was 6.86 times higher than the maximum values for the same parameter for other earthquakes at a frequency near the first natural frequency of the structure. Also, using time-frequency curve, it was shown that duration of the strong ground motion of all earthquake places within the dominant duration of the frequencies of the same earthquake was more than 10 sec.

Precipitation is one of the most important components of water cycle and plays an important role in assessing the climatic characteristics of each region. Estimates of monthly rainfall are important for various purposes such as flood estimation, drought, irrigation planning, and river basin management. In the present study, the monthly rainfall of Tabriz station was investigated using the intelligent Gaussian Process Regression (GPR) method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Wavelet Transform (WT). In this regard, different models were defined based on teleconnection patterns and climatic elements, and the impact of different input parameters was assessed. The obtained results from the models proved the high capability and efficiency of the applied method in predicting the monthly precipitation. It was observed that in prediction of the monthly precipitation, NAO, Nino3, MEI and climatic elements including mean monthly temperature and relative humidity, as well as precipitation related to the previous months, are effective in prediction and improve the models accuracy. The results showed that time series decomposition based on wavelet transformation led to more accurate outcomes compared to the complementary ensemble empirical mode decomposition. The best evaluation of test series using wavelet transform decomposition was obtained for the state of modeling based on teleconnection patterns and climatic elements with the values of DC=0.889, R=0.961 and RMSE=0.036. Also, based on the sensitivity analysis, Pt-3 was found to be the most effective parameter in modeling.

Recently, output only based real time identification of structures has attracted attention. One of the powerful offline system identification is the sparse component analysis (SCA) methods which are essentially a subset of blind source identification methods. This methods by transferring the dynamic response of the structure from time domain to frequency domain results in sparsity of the data and identifies the modal parameters of the system. In transferring data to the frequency domain, issues such as high data volume and continuous need for changing the time windows according to the variations in the input data result in limitations for real time identification. To overcome these problems, in this study, a wavelet transform-based sparse component analysis (WT-SCA) method is proposed for real time identification of structures. Then, using the proposed WT-SCA method and a semi-active tuned mass damper (STMD), an algorithm is proposed for developing a smart structure in a way that if any changes tend to occur in modal parameters of the structure due to a strong environmental excitation, the mechanical characteristics of the STMD using the WT-SCA method are retuned such that the system is stable and quite robust against the variations. The performance and accuracy of the proposed method are evaluated using numerical examples. Results demonstrate that the WT-SCA identities the real-time system state of the system with acceptable accuracy and reduces the dynamic response of structures equipped with STMD device effectively.

One of the methods of quake frequency investigation is the use of Fourier series and power spectrum. In this paper, the frequency of the major structural modalities in the earthquake is investigated using the power spectrum and the frequency of the earthquake, which has the greatest effect on the structure response. For this purpose, first, using the discrete wavelet theory, the acceleration of the earthquake is filtered up to 5 steps. At each stage of the filter, two waves of approximations and details are obtained. By analyzing the frequency content of approximate waves and details using the power spectrum and the Fourier spectrum, it results that the frequency of the wave is closer to the original wave of the earthquake. For convergence, the effect of frequency due to different frequencies in approximate waves is considered by the frequency effects of different frequencies in the main earthquake hypothesis called the approximation of the approximate wave to the maximum acceleration of the wave of the main earthquake. In order to verify the validity of the hypothesis, again, the Fourier spectrum and the power spectrum should be plotted for new approximation waves. After examining the frequency of the wave of approximations, it is concluded that the hypothesis works well. By studying the frequency content of the new approximation wave, it follows that for dynamic analysis, the frequency range of the power spectrum is more accurate than the Fourier spectrum. For this reason, the power spectrum is used for analytical calculations. Finally, with the investigations carried out in this paper, for the frequency domain analysis, the scalar approximation power spectrum is plotted. Then, several structures of the modal analysis and under the frequency spectrum of earthquake analysis are in the frequency domain. From the frequency response analysis curve and the results of the modal analysis, the frequency of the dominant mode of the structure is predicted. Using the wavelet transform, the number of accelerated earthquake mapping points reaches 1/32 But there was little mistake in estimating the prevailing frequency of the structure.

Assessment of pavement distresses plays a pivotal role in pavement management systems in determination of the most efficient option for repair and maintenance of the road. In the past decade, extensive researches have been made in order to develop automatic methods for pavement distresses processing based on computer vision techniques. Of the most important components comprising machine vision systems is the feature extraction procedure. In many image processing applications, textural features provide more effective information about the properties of the image regions in comparison with other features.  In the present study, three different algorithms were utilized with the purpose of statistically analyzing the textures of six different groups of asphalt pavement distress images. The first feature extraction algorithm is based on gray level co-occurrence matrix (GLCM) textural statistics. In second and third algorithms, the second-order textural descriptors of the images local patterns were extracted in spatial and wavelet domain, respectively. The distress categorization results based on a fusion of K-nearest neighbor (KNN) classifier and Mahalanobis distance, indicate that two level (double) regularizing the distress edges gray levels via employing wavelet transform and local binary pattern (third algorithm) outperforms other textural feature extraction algorithms in pavement distresses recognition and discrimination. The distress classification accuracy rate based on first, second and third algorithms were 61%, 75% and 97%, respectively.