جستجوی مقالات مرتبط با کلیدواژه "تشخیص خرابی" 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.

Infrastructures such as bridges, buildings, pipelines, marine structures, etc., play an important role in human life. Since major disasters in these structures, such as the collapse of bridges or buildings, often result in many casualties, damages, and social and economic problems, most industrialized countries allocate significant funds to monitor their health. Failure detection strategies and continuous monitoring of the structure's condition, especially after natural and manufactured disasters, make necessary measures to be taken in the early stages of failure and can reduce the cost of maintenance and the possibility of collapse. Structural health monitoring methods often provide an opportunity to reduce maintenance, repair, and retrofit costs during the structure's life cycle. Most of the structural health monitoring methods proposed and implemented to identify possible damages depend on the structure's dynamic characteristics. One of the most practical methods, which uses the results of time domain system identification to detect failure, is the damage locating vector (DLV) method. The DLV method aims to identify load combinations that result in zero strain fields for damaged members in both healthy and damaged structures. To accomplish this, we find a vector in the null space of the difference between the plasticity matrices of the two structures. The singular value analysis method is used on the plasticity difference matrix to calculate this space. The method involves applying the space vectors to the healthy structure and recording the internal stresses of the members, which are then converted into weighted normal stress (WSI) using statistical tools. The member with a lower WSI is more likely to be damaged. Since truss structures are usually used in bridges, long-span structures, as well, as a wide range of steel buildings with simple and braced frames, this research uses the covariance-based random subspace optimal method in identifying the modal characteristics, which is very efficient in low excitations, has been taken into consideration to check and monitor health during operation. To investigate the capability of the DLV method in the damage detection of these structures, a 5-story residential building with a simple steel frame was subjected to the Centro earthquake. According to the desired damage scenario, the second and fifth floors were introduced as the damaged floors in this earthquake by applying a 30 and 50% reduction in the cross-section. To account for uncertainty in the data collection, we included the mean root square of the second sensor's data in the results for sensors 3 and 5. As a result of this uncertainty, the damping error between 5 and 10% has been shown in the damaged and healthy structure. Using the method (SSI_ORT), it was observed that two DLV vectors were extracted. Further, with the increasing uncertainty of the random vibration test results, it was observed that the extraction DLVs could extract the possible damaged elements with high accuracy. Next, the effect of input and output noises on the results obtained from the DLV method was investigated. This study found that by increasing the SNR of the outputs by 15% while increasing the error of the extracted modal characteristics, the extracted DlVs also lose sufficient accuracy in diagnosing structural damage.

The structural health monitoring of the infrastructure before and during the operation allows the supervisors to detect damage in concrete structures in the first stage of damage initiation and help the society economically and sustainably. This article investigates pavement surface and subsurface monitoring methods using piezo-resistance sensors inside the concrete. At first, self-sensing concrete sensors' damage detection mechanisms were studied and investigated for different loading conditions. Next, the article focused on the reviews of several methods developed based on the piezo-resistive properties in concrete to evaluate the level of damage. These methods, such as fracture mechanics, sequential electrodes, resistor mesh model, and tomography methods, were considered in this research. In the end, the tomography method used to detect crack propagation in a concrete pavement under an accelerated loading test in the real-world example was investigated. The results showed that the tomography method could accurately detect damage even from the subsurface of the concrete pavement when the damages come up to the surface. Therefore, the accuracy and prediction of such methods will be the key to developing smart infrastructure and future ways.

Civil infrastructures, nowadays, are an indispensable part of society, and any unpredicted damage can cause severe economic and life loss. Hence, developing smart structures has been the topic of many studies during the past decades. In this article, developing a smart structure by synthesizing structural control and health monitoring is suggested by extracting damage-sensitive features from the active control force. The autoregressive models have been deployed to extract damage sensitive features in the time domain. Then, quadratic discriminant analysis is utilized to discriminant between different damage states of the structure. The active control force is obtained by two model reference adaptive controllers, namely the MIT rule and Lyapunov stability theorem, to attenuate the structural vibration caused by Gaussian white noise excitations. The proposed approach has been numerically studied on a three-story shear building with active ideal controllers in all floors. Results indicate that the proposed approach can detect the potential damage, as well as its severity and location precisely, and control forces subjected to controlled structures using active devices can be sensitive features for health monitoring purposes.

Diagnosing type and amount of deficits in beams and structure is highly important in terms of performance and impact. Despite of paramount studies on CFRP application, its usage as Strips can be mentioned as one of the most important ways. Using Half Scale in building structure and lab beams would provide more adducible results. Considering type of breakdown and its progress, the growth cracks on the surface of beams is different. In all constructions, the manner of implementation and the quality of materials are two important principles in terms of performance and efficiency of the structure. In present study, type breakdown in the beam of reinforce concrete is addressed in a nondestructive manner. In this technique, by using finite element method in identifying beam breakdown without creating an unstable condition, one can achieve a nondestructive situation in experiments which is significantly cost – effective. Hence, 16 250mm × 250mm × 2100mm concrete beams were built and divided into two groups with and without FRP composite. Each group has a control beam and remained samples have breakdown in concrete and rebar. Prepared beams were put in a 4 – point experiment and, upon gathering and analyzing relevant data, beams prototyping was begun. By using finite element method, all beams were prototyped in lab. The findings from prototyping and lab analyses are compared in the format of load – location change graph. One can consider the results of prototyping as a proper approximation of lab samples.

In recent decades, the science of structural health monitoring has played a key role in preventing damage and extending the life of structures. To conduct behavioral assessment, it is desirable to use tools that achieve sufficient accuracy with low cost. The processing of behavioral data requires methods that are able to identify and correctly troubleshoot different levels of damage from existing information. Nowadays, sensors are used to measure the behavior of structures including deformations and displacements and even deflections, but these sensors have some weak points. For example, Risk of damage to the sensor, pointwise and one-dimensional measuring, their data is difficult to analyze and using multiple or high-tech sensors becomes expensive. Optical behavior measurement and close-range photogrammetric operations have recently received attention due to their low cost and good accuracy. This method has some advantages like Indirect contact with objects, high-speed image capture, easy access to convenient digital cameras, low viewing costs, and the ability to process composite and instant data with easy operation. In addition, the high flexibility of this method in measuring accuracy and design capability to achieve predetermined accuracy is an important feature of this tool. Analytical methods are based on rules or equations that provide a clear definition of the problem. These methods work well in the cases which the rules are accurately clear and defined but there are many practical cases for which the rules are not known or it is very difficult to discover that calculations cannot be performed using analytical methods. Neural network is a generalizable model, which is based on the experience of a set of training data and therefore free of explicit law. Neural networks have the ability to collect, store, analyze, and process large amounts of data from numerical analyzes or experiments. Therefore, they have the ability to predict and build diagnostic models to solve various engineering problems and tasks In this paper, an attempt has been made to use this method to measure and troubleshoot laboratory model of a scaled suspension bridge that has a relatively complex behavior. For this purpose, the structure was subjected to uniform static loading in three step levels with three states: healthy and damaged in the deck and cables. Damages were created quite intentionally in the laboratory model, and from the information obtained, a database of bridge behavior in various situations was created. In order to assess the feasibility of using different methods in data processing and troubleshooting, first the data in the database were used in a simple linear method (direct comparison) and training in algorithms of machine learning methods. After that, deliberate damage was done again in the laboratory structure to allow testing the efficiency and accuracy of different methods. Finally, the accuracy, precision, and stability of the data processing methods of the support vector machine and artificial neural network were compared. The results showed that by object bundle justification of two-dimensional optical behaver measurement with close-range photogrammetry, a guaranteed accuracy of 0.0021 mm could be achieved. Using intensity image processing seems helpful to ease the calculation. Using high number of nodes in hidden layer makes it more difficult and time-consuming to train the neural network. In the first level of processing, the detection of the presence or absence of damage was associated with the complete superiority of neural networks with 100% accuracy and in the second level, the detection of the affected area, depending on the type of processing, the neural network with hyperbolic tangent transfer function archived 93% accuracy and the support vector machine archived 68% of the accuracy.

Wind energy is one of the most hopeful renewable energy sources that is also growing. The wind turbine tower supports the complete wind turbine system, and its damage may cause catastrophic failure of the wind turbine. However, the background to the study of the health monitoring of the wind turbine tower against its mechanical installations is insignificant. Besides, no comprehensive research has been conducted on the health monitoring of the tower with soil-structure interaction included. In this article, the extensive analysis of the multilevel 2D wavelet decomposition approach using orthogonal wavelets and soil-structure interaction is performed numerically. The established finite element model is calibrated and verified with the NREL 5-MW reference onshore wind turbine. Then, while defining several damage scenarios, the 3-dimensional modes of the finite element model of the damaged structure were investigated using the proposed method. The findings imply that the quality of the damage detection in the soil-structure interaction models has increased, generally.

Most building structures are damaged over time under environmental conditions and external loads. In this regard, the occurrence of damage is common and the detection of damage is the subject of much research. In this regard, wavelet conversion, 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, free vibrations of a four-story building with specified boundary conditions and monitored the health of the building based on experimental results using the continuous wavelet analytical method are studied and the damage that may occur in these structures were evaluated and analyzed. The finite element software is used to Model of the Building by the sandwich model. In this four-story building, eight-layer sandwich panel (polystyrene, concrete, steel, concrete) is used symmetrically. The fourteen natural frequencies of the sandwich structure were compared with the experimental model and the main modes of the structure were obtained to influence the health of the structure. An error of less than 2.5% reveals a good match between the results of the two models. Precast panel health monitoring results show that based on the experimental results, the damage location using the coif5 function with scale parameter 8 has been successfully identified and showed a higher perturbation of the coefficients at the damage locations than the other functions. Thus, the relative maximum and minimum jumps in the wavelet coefficients occurred at the location of the damage and considering the maximum or minimum wavelet coefficients generated at the damage location as the center of damage, the damage center can be identified with an error of less than 8%. Also, effects of higher modes are more pronounced in the damage intensity index as in the torsional modes of the structure, the maximum wavelet coefficients are greater and the intensity of the damage more pronounced.

Wind has been one of the cleanest sources of energy. The tendency to use wind turbines has been a growing trend in the world in recent decades. The size and capacity of wind turbines are increasing rapidly in order to obtain more wind energy. Statistics show that more giant turbines are more broken down and require more maintenance. The goal of wind farm owners is to monitor work to reduce downtime and increase the efficiency of each wind turbine. The wind turbine tower carries the entire wind turbine and is the second-largest cost of the wind turbine. Damage to the tower can endanger the entire wind turbine and cause extensive damage. However, the background to the study of the health monitoring of the wind turbine tower against its mechanical installations is insignificant. Besides, no comprehensive research has been conducted on the health monitoring of the tower with soil-structure interaction included. In this study, biorthogonal wavelets were used to process the mode shape of the damaged tower. The foundation is a square concrete foundation 20 m × 20 m and 1 m in depth. Two different soils, a normally consolidated clay and dense sand, are considered. Eighteen failure scenarios were defined. The results of this study indicate that the use of side-to-side mode shapes of the tower has a tangible advantage over its fore-aft mode shapes for detecting the failure. Considering the desirable effect of soil-structure interaction on damage detection, it is necessary to examine this effect in the analysis.

In this paper, the damage detection of single tapered pole is studied. Structural damage has been identified using the vibration method. In this method, the APSO was used to detect the location and severity of the damage. The objective function for detecting damages, is a correlation coefficient based on natural frequencies. In order to accelerate the calculation of the natural frequencies of the structure, iterative method was used. In this paper, the damage is applied to the structure, by reduction of the stiffness matrix of the element in a finite element modeling and also in order to match the real situation, error in frequency is considered. To evaluate the efficiency and robustness of the proposed method in detecting damage of tapered poles, two numerical examples, including a police surveillance camera pole and a water storage pole under different damaged scenarios, are examined. In first and second example, structures are divided by 15 and 25 elements, respectively, with uniform moment of inertia. The results show that the proposed method is capable of detecting both location and severity of the damage properly, despite the complexity of some damage scenarios. Therefore, the algorithm can be used to detect the damage of other structures.

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.

Using control devices to enhance system identification and damage detection in a structure requiring both vibration control and structural health monitoring is an interesting yet practical topic. In recent decades several independent research studies have been conducted on the semi-active vibration control and health monitoring of the civil structures subjected to earthquakes, whereas both require similar software. Therefore, where there is a need for both vibration control and health monitoring systems in a building structure, integrating both systems using common equipment would be economical. Application of the viscous dampers for control of buildings, due to their proper seismic performance, lack of significant increase in the structural mass, and least interference with the present situation of the building is rapidly increasing. For this reason, the main goal of this research is to present a new integrated procedure for simultaneous health monitoring and semi-active control of the building structure using the viscous dampers with variable damping coefficients. Next, a scheme is presented for updating the building characteristic matrices and identifying the structural parameters. Finally, according to the updated system matrices, the efficiency of semi-active viscous dampers is investigated. For this purpose, a linear quadratic regulator optimal control algorithm is used for a building subjected to seismic excitations. The results show that the structural parameters are accurately identified; and, at the same time, the building structural vibration is significantly reduced.

In this paper, a crack detection method is presented to detect Euler-Bernoulli beams containing arbitrary number of transverse cracks. The proposed method uses the time domain signal of the free vibration response to provide the position and depth of cracking of the Euler-Bernoulli beam that is modeled with a modified stiffness using the Spring model, with high accuracy and precision. The time history responses used in this paper are nodal computational accelerations at certain points of the beam exposed to impact load. The acceleration of the nodes is calculated with the Newmark beta method at the edge of the elastic beam`s superelements. Initially, using the computational time history of the damaged beam and the analytical model of the Euler-Bernoulli beam, the objective function of the failure detection problem, to be optimized by particle swarm algorithm, is defined and, intensity and location of transverse cracks are calculated by solving the optimization problem in Matlab environment. In order to determine the accuracy of the proposed method, three beam samples with different cracks and loadings are considered. In the first sample, the crack supposed to be in the superelements of beam and the beam considered to be with four elements as superelements. The second one has ten elements and same loading as previous. The third one has twenty elements and the loading is on the second element. All of the loadings are impulse loads. The comparison of the results of a four elements beam with the primitive conditions shows that accuracy of the deducted results were exactly matched. For the ten element beam, the results were satisfying but in the twenty element beam with asymmetric loading, obtained results indicate imprecise match.</div>

To determine the accuracy of the developed model in real environmental conditions, different percentages of noise were added to the data of all three samples. These noise addition to data, contain 1, 3, 5 Percentages of noise.  The results show that the model presented in the presence of noise also provides accurate results and the model is not sensitive to the presence of noise in the data applied to three samples. Considering different number of elements in each sample, no convergence was observed, Also the results were not sensitive to the location of impact load applied on the samples.</div>

The results of this study indicated that asymmetric cracking and loading variation are very effective in predicting beam failure. The results also indicate that variable reduction is very effective on the accuracy of results.</div>

Having more cracks and therefore more elements to analyze will yield to less accurate results. To lessen these inaccuracies, it can be practical to achieve better results by assuming the location of crack is constant in the element length if the depth of crack be the matter of importance.</div>

The number of iterations that have been executed, indicate that the pace of convergences in the developed process is less than when PSO deployed solely. This speed rate for obtaining results makes the developed method practical for solving crack detection problems in structures.</div>
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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. Wavelets are a combination of a family of basic functions that are capable of detecting signals in the time (or location) and frequency (or scale) range. In fact, wavelet transform is based on the principle that any signal can be transformed into a set of local functions called wavelets. Any local feature of a signal can be analyzed using the corresponding wavelet functions. The wavelet transforms to the singularities points in the signals are sensitive and can be used to detect abrupt changes in modes, which often indicate damage. In this study, free vibrations of a four-story building with specified boundary conditions have been investigated and monitored the health of the building basis on experimental results using the continuous wavelet analytical method are studied and the damage that may occur in these structures has been evaluated and analyzed. Building modeling is done in finite element software using the sandwich model. In this four-story building, eight-layer sandwich panel (polystyrene, concrete, steel, concrete) is used symmetrically. The fourteen natural frequencies of the sandwich structure were compared with the experimental model. and the main modes of the structure obtained to influence the health of the structure. An error of less than 2.5% reveals a good match between the results of the two models. Changes in the values of natural frequencies and also the inconsistency of the modes shape، based on Modal Assurance Criterion (MAC) and the angle between modes of shape confirm the damage of the structure. Precast panel health monitoring results show that based on the experimental results, the damage location using the coif5 function with scale parameter 8 has been successfully identified and shows a higher perturbation of the coefficients at the damage locations than the other functions. Thus, the relative maximum and minimum jumps in the wavelet coefficients occurred at the location of the damage and considering the maximum or minimum wavelet coefficients generated at the damage location as the center of damage, the damage center can be identified with an error of less than 8%. The disturbance of the wavelet coefficients of each of the damage locations are independent of the other damage locations with different intensities. Also, the effect of higher modes is more pronounced in the damage intensity index as in the torsional modes of the structure, the maximum wavelet coefficients are greater and the intensity of the damage is increased. In addition, in the process of reducing the structural stiffness, the first and second stories play a more important role, and around the openings are the critical points of the structure.

Damage detection is a necessary part for structural health monitoring (SHM), being beneficial to SHM and determining the severity of damage. Application of statistical pattern recognition methods for SHM has gained considerable attention to detect changes in a structure. One of the advantages of these methods is that only data from undamaged state is needed in training phase (unsupervised learning) as opposed to supervised learning where data from both undamaged and damaged conditions is required to train the model. There are different approaches used by researchers and the success of a certain one may depend on the type of structure or structural changes. Most of studies focused on the application of statistical pattern recognition methodologies for SHM utilize the time series analyses for extracting damage-sensitive features. These features are statistical properties of time series models that directly depend on damage. Extracting damage-sensitive features is a fundamental step in damage detection process because pattern recognition algorithms can identify the state of structure unless these features are just dependent on damage.  The change in an environmental and operational condition during the data acquisition process is one of the problems that causes damage features to be depended on factors besides existence of damage. This can lead to incorrect structural state identification. On the other hand, after extracting damage-sensitive features, the application of a statistical novelty detection methodology for decision making on structural state is a significant topic in SHM. This paper proposes a new application of random decrement (RD) technique in order to choose appropriate and accurate damage features which are independent from environmental and operational conditions of structure. The RD technique transforms time series data of the structural response to free decay vibration form that only consist of dynamic properties information by averaging them at specific time. Moreover, a novel statistical method named as Bhattacharyya measure is applied as a robust method for damage diagnosis. The Bhattacharyya measure determines the discrepancy between damage features from different structural states through partitioning data and utilizing numerical information of each partition. Herein, before extracting damage features, time series data are averaged through RD technique. Then the Autoregressive-Autoregressive with exogenous output model (AR-ARX) is used to fit a mathematical model to the averaged time series data and the residuals are considered as damage features. The Bhattacharyya measure is utilized for damage identification and localization. The data obtained from an experimental study on a three-story frame structure model are exploited to validate the accuracy and reliability of the proposed algorithm. Random excitation is applied by varying amplitude level of the input force, simulating various environmental and operational conditions. Damage is induced at two different locations. The proposed algorithm is conducted on data from various environmental and operational conditions at two different locations. A comparative study is also carried out to demonstrate the superiority of the proposed algorithm over some exiting techniques. Results show that the application of random decrement technique reduces the influence of operational and environmental condition due to averaging and normalizing data and correctly determines the state of structure. In addition, using Bhattacharyya measure improves the structural health monitoring results in damage identification and localization.

Damage Detection methods based on signal are principal and widely used methods that contain wavelet packet decomposition, which is one of new methods in this field. On the other hand there are lots of methods and implements for evaluate models which classify data and regression.Random Forests ,which is newly used method has attracted researchers attention. In this paper a experimental structure was designed and analyzed. drifts from time history response were decomposed to energy rate indexes by wavelet packet decomposition. energy rate indexes in each damage conditions were classified in 3 class of damage conditions and they made data base. finally by training the algorithm, R-F tested other conditions with comparing data base and other near damage conditions, and classified in one of 3 classes. Random Forests precision in this research was 83% which is admissible for classifying. this algorithm can be used on other researches in the future time.

Structural damage detection is based on that the dynamic response of structure will change because of damage. Hence, it is possible to estimate the location and severity of damage leads to changes in the dynamic response before and after the damage. In this study, the genetic fuzzy system has been used for bridge structural health monitoring. A key objective of using genetic algorithms is to automate the design of fuzzy systems. This method is used for damage detection of a single span railway bridge with steel girders and a concrete bridge. For studying damage detection, the numerical models of these two bridges are built with the measured dynamic characteristics. A three-dimensional finite element model and a single two-dimensional girders model of the bridge have been constructed to study usefulness of the genetic fuzzy system for damage detection and the effectiveness of modeling. After analysis to control the uncertainties, the measured frequencies are contaminated with some noise and the effect of that on the achievement of damage detection method is evaluated. The present study has shown that the natural frequency has appropriate sensitivity to different damage scenarios in the structure. In addition, the natural frequency in comparison with other modal parameters, is less affected by random noise. Increasing the number of measurement modes and using torsional modes, will lead to an accurate damage diagnosis even in symmetrical structures.

One of the methods for damage detection is dynamic methods based on the signal, which can be used to detect damage in the structures through signal processing. In this study, wavelet transform is used in order to determine the location and extent of the damage in steel plate shear walls so that the mode shape of modal analysis is decomposed as the input signal by wavelet transform and in the case of damage, it is possible to identify its accurate location. As a result, by using this transform without information of healthy structure, the possible damage scenarios that occur in the structure can be detected through disturbances which are created in the signal or the mode shapes of steel plate shear walls.