جستجوی مقالات مرتبط با کلیدواژه « تشخیص آسیب » در نشریات گروه « عمران »

Damage to the structure during operation has always been possible, and therefore the issue of monitoring the health of important structures in order to control and manage safe operation has received attention in recent years. The process of extracting the parameters involved in identification the inherent characteristics of the target structural systems in order to monitor and detect damages is possible with different methods which have been introduced and investigated under the title of environmental modal analysis, which have been developed both in the time domain and in the frequency domain. Among the time domain methods, we can mention the stochastic subspace identification (SSI) method. The stochastic subspace identification method is one of the Output Only Modal Analysis (OOMA) methods that has been taken into consideration assuming the existence of uncertainty in modeling as well as noise in data observation and measurement, and system parameters are identification by applying statistical relationships to the output data. Due to the high accuracy of the covariance-drived stochastic subspace (SSI-cov) method which performs data monitoring by constructing the covariance function related to the recorded output data, therefore this sub-method has been used. Due to the limitation in the number of sensors that can be installed in real structures, the use of the Reference-based covariance-drived stochastic subspace identification (SSI-cov-ref) method will make it possible to identification the structure under investigation with a limited number of sensors and if there is damage, Its location can be recognized. The efficiency of the reference-based covariance-drived stochastic subspace identification method with a more limited number of sensors can also be presented. In order to damage detecting in the structure, the method of measuring changes in the modal strain energy of the members in healthy and damaged states has been used, and an index called modal strain energy has been defined and presented But in the case of damage in the structure, the undamaged members will also have strain energy due to the strain effect of the damaged members. Therefore, by using the probabilities approach, it will be possible to provide an index of the probability of structural member damage under a specific failure scenario using different information sources based on the mode shapes. In the following, using the genetic algorithm in the form of an optimization problem, the installation location for the available sensors for the target structure is optimized and presented. During the current research, after finite element modeling in MATLAB software and dynamic analysis of several samples of structure with the assumption of installing a limited number of sensors ,and the results obtained from the output of the reference-based covariance-drived stochastic subspace identification method using the changes in the modal strain energy of the members And the Bayesian strategy has been used in the damage detection in the affected members, and the efficiency of the proposed method has been discussed in the framework of the mentioned process. Based on the results obtained from the output of the proposed process, the affected members will be identified and distinguishable.
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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.

Occurrence of the nonlinear behavior can be a sign of changes in structural parameters and the presence of damage in systems. This paper presents a method for detecting and quantifying of nonlinearity, as an indication of damage, using the indicators that are extracted from the frequency response functions (FRFs) and Hilbert transform of FRFs, for steel moment frame structural systems. Using time history analysis under selected harmonic ground motions, the results of FRFs for the studied 4-story system are illustrated and discussed.Nonlinear behavior is a result of formation plastic hinges under earthquake loading. FRFs and Hilbert transform of FRFs are extracted from both the linear and nonlinear behavior of 4, 8, and 12-stories steel moment frames under fifteen different earthquake records with different characteristics in their time histories. Some near and far field well-known earthquakes records have been selected for the present study as the ground motions input in time history analysis. Different levels of nonlinearity are determined based on the maximum rotation of hinges in column members of structures equal to 2θy, 4θy and 6θy, in which θy is yield limit rotation. The indicators of the studied systems are calculated and evaluated for linear and different levels of nonlinearity based on the mathematical power of changes for FRFs and Hilbert transform of FRFs. The presented indicators are extracted based on the frequency response functions (FRFs) and Hilbert transform of FRFs for the responses of absolute acceleration and relative displacement of stories. The indicators are calculated at the location of acceleration sensors (accelerometer) in four levels of the structural systems, while the formation of plastic hinges in the columns of the structures will occur only at the level of the distance between the adjacent sensors.It is shown that the proposed method and calculated indicators have enough accuracy and sensitivity in detecting the “existence”, “location” and “extent” of damage.

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.

One way to diagnose damage in structures is to use their modal strain energy change index. Since occurrence of damage in parts of a structure, in addition to changing the modal strain energy of the damaged parts, also changes the modal strain energy of the healthy parts, in the vicinity of the main damaged elements, false damaged elements will appear, which makes it difficult to identify the main damaged elements. To solve this problem, in the present work a two-step method has been used to determine the location of the damage on the deck of a concrete bridge. In the first step, by finite element modelling and performing the modal analysis, the initial damage index of different parts of the bridge deck is determined based on modal strain energy changes, using a few vibration mode shapes. In the second step, using weight coefficients, the modified damage index for each element is calculated separately, minimizing the number of false damaged elements. The proposed method for calculating weight coefficients is simple and at the same time accurate. The efficiency of this method was investigated in several damage scenarios with an intensity of only 5% in different parts of the deck of bridge. The results showed that, while the initial damage index could not distinguish even 3 damaged elements in the bridge deck from the healthy elements, the modified damage index was able to identify up to 7 damaged elements in different parts of the bridge deck with good accuracy using only 2 mode shapes.

The damage detection (DD) of the bridge has always been major concerns for engineers. This paper attempts to detect damage in the continuous deck bridges by providing a new method based on acceleration responses and their instantaneous amplitudes. The DD in this paper has two steps: firstly, determining the vicinity of damage in global DD. Secondly, determining the location of damage in local DD. Then by acceleration signals, the instantaneous amplitude values of healthy and damaged structural responses extracted via HHT. Further, for accurate evaluation of the proposed method, damage locations are determined by the cross-correlation damage index (DICC). To assess the feasibility and reliability of proposed methods, several analytical models of concrete bridges of one to three-spans, as well as experimental model a simply supported steel beam, have been used. In order to consider noise pollution during data acquisition, a certain amount of noise is added to the response. The results in the analytical and experimental models showed that the proposed methods can determine the damage locations with appropriate accuracy for different damage scenarios and it could provide more exact results with a rapid estimation.

Changes in modal strain energy of elements before and after damage is a robust damage detection index. However, in structures like double layer grids which have large number of elements, this method has some problems. First, In large structures this method needs more mode shapes to detect damage through modal strain energy method which in practice is difficult to determine. Second, this method introduce some healthy elements as damaged element. To overcome these problems, in this paper a two stage damage detection technique based on modal strain energy method is presented for detecting damage in double layer grids. First, the Modal Strain Energy Based Index (MSEBI) for each mode shape is determined. Then a data fusion technique based on Bayesian theory is used to combine MSEBI values obtained from each mode shape to find damaged elements. Then Charged System Search (CSS) optimization method which is a powerful optimization method is employed to optimize an objective function based on natural frequency to determine damage severity of damaged elements. To demonstrate the performance of the proposed method, a large double layer grid with 1536 elements and different single and multiple damage cases is considered. Numerical results show that the proposed method can successfully find damaged elements and their severities using only few first numbers of mode shapes and frequencies.

Structural health monitoring is a procedure to provide accurate and immediate information on the condition and efficiency of structures. There is variety of damage factors and the unpredictability of future damage, is a necessity for the use of structural health monitoring. Structural health monitoring and damage detection in early stages is one of the most interesting topics that had been paid attention because the majority of damages can be repaired and reformed by initial evaluation ,thus the spread of damage to the structures, building collapse and rising of costs can be avoided .Detection of concrete shear wall damages are designed to withstand the lateral load on the structure is critical .Because failures and malfunctions of shear walls can lead to serious damage or even progressive dilapidation of concrete structures .Change in stiffness and frequency can clearly show the damage occurrence. Mathematical transformation is also a tool to detect damage. In this article, with non- linear time history analysis, the finite element model of structures with concrete shear walls subject to four earthquakes have extracted and using Fourier and wavelet transform, the presence of shear walls is detected at the time of damage.

I n t h i s s t u d y، i n o r d e r t o d e t e r m i n e t h e t i m e a n d l o c a t i o n o f d a m a g e o c c u r r e n c e i n m o m e n t-r e s i s t i n g f r a m e s، a m e t h o d، b a s e d o n a g e n e r a l c o n c e p t o f s i g n a l p r o c e s s i n g m e t h o d s، i s p r e s e n t e d. M o s t p r e v i o u s l y p r e s e n t e d m e t h o d s a i m e d a t f i n d i n g t h e t i m e a n d l o c a t i o n o f d a m a g e b y c o n s i d e r i n g o c c u r r e n c e c h a n g e s i n s t r u c t u r a l r e s p o n s e s u s i n g F o u r i e r، s h o r t t i m e F o u r i e r o r w a v e l e t t r a n s f o r m m e t h o d s. V i b r a t i o n p r o p e r t i e s o f t h e s y s t e m m a y b e a d d r e s s e d a s t h e v i b r a t i o n m o d a l f r e q u e n c i e s o r m o d a l s h a p e s o f t h e s t r u c t u r e. I n t h e p r o p o s e d m e t h o d، w a v e l e t a n a l y s i s o f m o r e c o m p l e x r e s p o n s e s o f t h e s t r u c t u r e، i. e. r o t a t i o n a l، a n g u l a r v e l o c i t y o r a c c e l e r a t i o n r e s p o n s e s o f t h e j o i n t s (n o d e s o f t h e f r a m e)، i s e m p l o y e d t o e x t r a c t i n f o r m a t i o n a b o u t d a m a g e. T h e s e f u n c t i o n s c o n t a i n m o r e i n f o r m a t i o n، s u c h t h a t w a v e l e t a n a l y s i s m a y b e c o n d u c t e d b a s e d o n o n l y t h e f i r s t l e v e l o f s i g n a l d e c o m p o s i t i o n. T o d e t e r m i n e t h e l o c a t i o n o f d a m a g e، t h e c o n c e p t o f a n a m p l i t u d e s p e c t r u m i s u s e d، w h i c h i s a c h i e v e d f r o m c o m p a r i s o n o f t h e a m p l i t u d e s p e c t r a o f t h e r o t a t i o n a l r e s p o n s e s o f t h e t w o a d j a c e n t n o d e s. S i n c e، b y o c c u r r i n g d a m a g e، t h e r e i s a l a r g e d i f f e r e n c e b e t w e e n t h e s e a m p l i t u d e s p e c t r a، t h e r e i s n o d o u b t t h a t t h e l o c a t i o n o f d a m a g e i n t h i s m e t h o d w i l l b e f o u n d. I n t h i s r e s e a r c h، t h e r e i s n o n e e d t o k n o w t h e p r i m a r y s y s t e m p r o p e r t i e s، a n d t h i s i s t h e m a i n a d v a n t a g e o f t h e m e t h o d. T h e o t h e r a d v a n t a g e i s t h a t t h e t i m e o f d a m a g e i s a c h i e v a b l e، b e c a u s e i t s t i m e a n d l o c a t i o n i s d i r e c t l y c o m p u t e d f r o m t h e m e a s u r e d e a r t h q u a k e r e s p o n s e s; t h e r e i s n o n e e d t o c o n d u c t a n y (a m b i e n t o r f o r c e d) v i b r a t i o n t e s t s. E x t e n s i v e a n a l y s e s i n d i c a t e t h e a c c u r a c y a n d p o w e r o f t h e p r o p o s e d m e t h o d t o s u c c e s s f u l l y d e t e r m i n e t h e t i m e a n d l o c a t i o n o f d a m a g e d u r i n g d i f f e r e n t l o a d i n g s. A c c o r d i n g t o t h e a b i l i t y o f t h i s m e t h o d t o d e t e c t s p e c i f i c a t i o n s o f d a m a g e a t e a r l y s t a g e s، i t i s r e c o m m e n d e d f o r t h e h e a l t h m o n i t o r i n g o f i m p o r t a n t s t r u c t u r e s، w h o s e p e r f o r m a n c e s a r e s t r o n g l y c o n s i d e r e d d u r i n g a n d a f t e r s t r o n g g r o u n d m o t i o n e a r t h q u a k e s.

I‌n t‌h‌i‌s r‌e‌s‌e‌a‌r‌c‌h, a n‌e‌w m‌o‌d‌i‌f‌i‌e‌d m‌a‌t‌r‌i‌x s‌u‌b‌t‌r‌a‌c‌t‌i‌o‌n m‌e‌t‌h‌o‌d i‌s p‌r‌o‌p‌o‌s‌e‌d u‌s‌i‌n‌g s‌q‌u‌a‌r‌e t‌i‌m‌e-f‌r‌e‌q‌u‌e‌n‌c‌y r‌e‌p‌r‌e‌s‌e‌n‌t‌a‌t‌i‌o‌n‌s t‌o d‌e‌t‌e‌c‌t b‌r‌i‌d‌g‌e p‌i‌e‌r d‌a‌m‌a‌g‌e a‌n‌d i‌t‌s l‌o‌c‌a‌t‌i‌o‌n. T‌h‌e n‌e‌w p‌r‌o‌p‌o‌s‌e‌d m‌e‌t‌h‌o‌d i‌s c‌o‌n‌f‌i‌r‌m‌e‌d, u‌s‌i‌n‌g t‌h‌e s‌e‌i‌s‌m‌i‌c r‌e‌s‌p‌o‌n‌s‌e o‌f t‌h‌e 448 m‌e‌t‌e‌r l‌o‌n‌g G‌h‌o‌t‌o‌u‌r B‌r‌i‌d‌g‌e m‌o‌d‌e‌l, a‌n‌d c‌o‌m‌p‌a‌r‌e‌d w‌i‌t‌h c‌o‌r‌r‌e‌l‌a‌t‌i‌o‌n a‌n‌d l‌e‌a‌s‌t s‌q‌u‌a‌r‌e d‌i‌s‌t‌a‌n‌c‌e m‌e‌t‌h‌o‌d‌s. T‌i‌m‌e f‌r‌e‌q‌u‌e‌n‌c‌y p‌l‌a‌n‌s o‌f d‌a‌m‌a‌g‌e‌d a‌n‌d n‌o‌n-d‌a‌m‌a‌g‌e‌d b‌r‌i‌d‌g‌e m‌a‌t‌r‌i‌x e‌l‌e‌m‌e‌n‌t‌s a‌r‌e c‌a‌l‌c‌u‌l‌a‌t‌e‌d u‌s‌i‌n‌g r‌e‌d‌u‌c‌e‌d i‌n‌t‌e‌r‌f‌e‌r‌e‌n‌c‌e d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n. T‌h‌e d‌i‌f‌f‌e‌r‌e‌n‌c‌e m‌a‌t‌r‌i‌x i‌s c‌a‌l‌c‌u‌l‌a‌t‌e‌d b‌y s‌u‌b‌t‌r‌a‌c‌t‌i‌o‌n o‌f c‌o‌r‌r‌e‌s‌p‌o‌n‌d‌i‌n‌g m‌a‌t‌r‌i‌x e‌l‌e‌m‌e‌n‌t‌s. T‌h‌e p‌o‌s‌s‌i‌b‌i‌l‌i‌t‌y o‌f a‌n e‌x‌i‌s‌t‌i‌n‌g d‌a‌m‌a‌g‌e i‌n‌d‌e‌x i‌s c‌a‌l‌c‌u‌l‌a‌t‌e‌d b‌y s‌u‌m‌m‌a‌t‌i‌o‌n o‌f a‌l‌l e‌l‌e‌m‌e‌n‌t‌s o‌f d‌i‌f‌f‌e‌r‌e‌n‌c‌e m‌a‌t‌r‌i‌c‌e‌s a‌n‌d t‌h‌e‌n n‌o‌r‌m‌a‌l‌i‌z‌i‌n‌g t‌h‌e‌m w‌i‌t‌h a m‌a‌x‌i‌m‌u‌m v‌a‌l‌u‌e. L‌i‌n‌e‌a‌r t‌i‌m‌e h‌i‌s‌t‌o‌r‌y a‌n‌a‌l‌y‌s‌e‌s a‌n‌d e‌a‌r‌t‌h‌q‌u‌a‌k‌e a‌c‌c‌e‌l‌e‌r‌a‌t‌i‌o‌n r‌e‌c‌o‌r‌d‌s o‌f S‌a‌n F‌e‌r‌n‌a‌n‌d‌o, L‌o‌m‌a P‌r‌i‌e‌t‌a a‌n‌d N‌o‌r‌t‌h‌r‌i‌d‌g‌e e‌a‌r‌t‌h‌q‌u‌a‌k‌e‌s a‌r‌e u‌s‌e‌d f‌o‌r s‌e‌i‌s‌m‌i‌c r‌e‌s‌p‌o‌n‌s‌e a‌n‌a‌l‌y‌s‌i‌s. I‌n p‌r‌e‌v‌i‌o‌u‌s r‌e‌s‌p‌o‌n‌s‌e a‌n‌a‌l‌y‌s‌e‌s o‌f t‌h‌e G‌h‌o‌t‌o‌u‌r B‌r‌i‌d‌g‌e, a‌s s‌t‌a‌t‌e‌d i‌n r‌e‌f‌e‌r‌e‌n‌c‌e [23], t‌h‌e t‌h‌i‌r‌d p‌i‌e‌r f‌r‌o‌m t‌h‌e l‌e‌f‌t i‌s c‌o‌n‌s‌i‌d‌e‌r‌e‌d m‌o‌r‌e v‌u‌l‌n‌e‌r‌a‌b‌l‌e t‌o s‌e‌i‌s‌m‌i‌c d‌a‌m‌a‌g‌e. T‌h‌e‌r‌e‌f‌o‌r‌e, f‌o‌r t‌h‌e p‌u‌r‌p‌o‌s‌e o‌f t‌h‌i‌s s‌t‌u‌d‌y, a r‌e‌d‌u‌c‌t‌i‌o‌n o‌f t‌h‌i‌r‌t‌y p‌e‌r‌c‌e‌n‌t i‌n t‌h‌e s‌t‌i‌f‌f‌n‌e‌s‌s o‌f t‌h‌a‌t p‌i‌e‌r i‌s c‌o‌n‌s‌i‌d‌e‌r‌e‌d, t‌o s‌i‌m‌u‌l‌a‌t‌e t‌h‌e s‌e‌i‌s‌m‌i‌c d‌a‌m‌a‌g‌e i‌n t‌h‌e d‌a‌m‌a‌g‌e‌d a‌n‌a‌l‌y‌t‌i‌c‌a‌l m‌o‌d‌e‌l. I‌t i‌s s‌h‌o‌w‌n t‌h‌a‌t t‌h‌e p‌r‌o‌p‌o‌s‌e‌d m‌e‌t‌h‌o‌d c‌o‌u‌l‌d s‌a‌t‌i‌s‌f‌a‌c‌t‌o‌r‌i‌l‌y i‌d‌e‌n‌t‌i‌f‌y t‌h‌e d‌a‌m‌a‌g‌e l‌o‌c‌a‌t‌i‌o‌n. F‌o‌r t‌h‌e s‌e‌i‌s‌m‌i‌c r‌e‌s‌p‌o‌n‌s‌e o‌f t‌h‌e t‌o‌p o‌f t‌h‌e b‌r‌i‌d‌g‌e p‌i‌e‌r‌s, t‌h‌e m‌a‌x‌i‌m‌u‌m e‌r‌r‌o‌r i‌n l‌o‌c‌a‌t‌i‌n‌g t‌h‌e d‌a‌m‌a‌g‌e i‌s 6 p‌e‌r‌c‌e‌n‌t, w‌h‌i‌l‌e, f‌o‌r t‌h‌e s‌e‌i‌s‌m‌i‌c r‌e‌s‌p‌o‌n‌s‌e o‌f t‌h‌e m‌i‌d‌d‌l‌e o‌f t‌h‌e b‌r‌i‌d‌g‌e p‌i‌e‌r‌s, i‌t i‌s 14 p‌e‌r‌c‌e‌n‌t.T‌h‌e t‌i‌m‌e-f‌r‌e‌q‌u‌e‌n‌c‌y r‌e‌p‌r‌e‌s‌e‌n‌t‌a‌t‌i‌o‌n‌s u‌s‌e‌d i‌n‌c‌l‌u‌d‌e: t‌h‌e s‌h‌o‌r‌t t‌i‌m‌e F‌o‌u‌r‌i‌e‌r t‌r‌a‌n‌s‌f‌o‌r‌m s‌p‌e‌c‌t‌r‌u‌m, w‌a‌v‌e‌l‌e‌t t‌r‌a‌n‌s‌f‌o‌r‌m s‌p‌e‌c‌t‌r‌u‌m, W‌i‌g‌n‌e‌r-V‌i‌l‌l‌e D‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n, C‌h‌o‌i-W‌i‌l‌l‌i‌a‌m‌s d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n, s‌m‌o‌o‌t‌h‌e‌d p‌s‌e‌u‌d‌o W‌i‌g‌n‌e‌r-V‌i‌l‌l‌e d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n a‌n‌d r‌e‌d‌u‌c‌e‌d i‌n‌t‌e‌r‌f‌e‌r‌e‌n‌c‌e d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n, w‌h‌i‌c‌h a‌r‌e f‌i‌n‌a‌l‌l‌y i‌d‌e‌n‌t‌i‌f‌i‌e‌d a‌s o‌p‌t‌i‌m‌a‌l p‌e‌r‌f‌o‌r‌m‌a‌n‌c‌e t‌i‌m‌e-f‌r‌e‌q‌u‌e‌n‌c‌y e‌p‌r‌e‌s‌e‌n‌t‌a‌t‌i‌o‌n f‌o‌r b‌r‌i‌d‌g‌e s‌e‌i‌s‌m‌i‌c r‌e‌s‌p‌o‌n‌s‌e s‌i‌g‌n‌a‌l p‌r‌o‌c‌e‌s‌s‌i‌n‌g. R‌e‌d‌u‌c‌e‌d i‌n‌t‌e‌r‌f‌e‌r‌e‌n‌c‌e d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n a‌n‌d W‌i‌g‌n‌e‌r-V‌i‌l‌l‌e d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n a‌r‌e b‌o‌t‌h i‌n t‌h‌e C‌o‌h‌e‌n c‌l‌a‌s‌s, b‌u‌t r‌e‌d‌u‌c‌e‌d i‌n‌t‌e‌r‌f‌e‌r‌e‌n‌c‌e d‌i‌s‌t‌r‌i‌b‌u‌t‌i‌o‌n m‌e‌t‌h‌o‌d‌s a‌r‌e m‌o‌r‌e a‌p‌p‌r‌o‌p‌r‌i‌a‌t‌e f‌o‌r p‌r‌o‌c‌e‌s‌s‌i‌n‌g s‌e‌i‌s‌m‌i‌c b‌r‌i‌d‌g‌e t‌r‌a‌n‌s‌i‌e‌n‌t n‌o‌n‌s‌t‌a‌t‌i‌o‌n‌a‌r‌y r‌e‌s‌p‌o‌n‌s‌e s‌i‌g‌n‌a‌l‌s. T‌i‌m‌e-f‌r‌e‌q‌u‌e‌n‌c‌y p‌l‌a‌n‌e‌s h‌a‌v‌e b‌e‌e‌n c‌a‌l‌c‌u‌l‌a‌t‌e‌d a‌n‌d d‌y‌n‌a‌m‌i‌c s‌p‌e‌c‌i‌f‌i‌c‌a‌t‌i‌o‌n‌s o‌f t‌h‌e s‌y‌s‌t‌e‌m h‌a‌v‌e b‌e‌e‌n e‌s‌t‌i‌m‌a‌t‌e‌d. T‌h‌e p‌r‌o‌p‌o‌s‌e‌d a‌l‌g‌o‌r‌i‌t‌h‌m i‌s a s‌e‌i‌s‌m‌i‌c o‌u‌t‌p‌u‌t-o‌n‌l‌y m‌e‌t‌h‌o‌d. T‌h‌e‌r‌e‌f‌o‌r‌e, i‌t h‌a‌s t‌h‌e a‌d‌v‌a‌n‌t‌a‌g‌e o‌f n‌o‌t n‌e‌e‌d‌i‌n‌g t‌o d‌e‌f‌i‌n‌e t‌h‌e b‌r‌i‌d‌g‌e a‌n‌a‌l‌y‌t‌i‌c‌a‌l m‌o‌d‌e‌l a‌n‌d m‌e‌a‌s‌u‌r‌i‌n‌g s‌e‌i‌s‌m‌i‌c i‌n‌p‌u‌t l‌o‌a‌d‌i‌n‌g, a‌n‌d, a‌l‌s‌o, n‌o‌t n‌e‌e‌d‌i‌n‌g t‌o u‌s‌e h‌a‌r‌m‌o‌n‌i‌c f‌o‌r‌c‌e‌d v‌i‌b‌r‌a‌t‌i‌o‌n a‌n‌a‌l‌y‌s‌i‌s a‌f‌t‌e‌r e‌a‌r‌t‌h‌q‌u‌a‌k‌e o‌c‌c‌u‌r‌r‌e‌n‌c‌e f‌o‌r b‌r‌i‌d‌g‌e s‌e‌i‌s‌m‌i‌c d‌a‌m‌a‌g‌e d‌e‌t‌e‌c‌t‌i‌o‌n, a‌s i‌s g‌e‌n‌e‌r‌a‌l i‌n s‌o‌m‌e o‌t‌h‌e‌r m‌e‌t‌h‌o‌d‌s.

In scientific research works, time domain and frequency domain methods have been widely used to detect damage and its location. By making attention to the capabilities of square time frequency representations in this study a new algorithm based on the representations was suggested for the first time to identify system and detect seismic damage in bridge piers. To evaluate the proposed algorithm, the analytical model of PEER-W180 Bridge was used. According to the method, before an earthquake event, some sensors were installed on top of the piers and vibrated the bridge by exciting force and recorded the bridge response signals. After the earthquake event which caused damages in the bridge piers, structural response signals of the piers were then recorded. Therefore the signals were processed using Reduced Interference Distribution, which is a time frequency representation, and dynamic characteristics were extracted. Then, by using the suggested threedimensional tensor method, the damage and its location were detected. The results show the ability of the proposed algorithm for system identification and seismic damage detection in bridge pier. The algorithm is an output-only method. Furthermore, there is no need to create and update an analytical model of an existing bridge. According to the results, the damaged pier was detected correctly using the proposed algorithm. In addition, maximum error in the probability of damage in the piers was calculated equal to % 6.1. Therefore, the results indicate the ability of the algorithm in detection the damage and its location.

This study is intended to present a method for the localization and evaluation of damage in structures based on changes in natural frequencies and mode shapes of the damaged structures using an optimization approach. The proposed method localizes and evaluates the damage (or damages) of structures using optimization of a objectivefunction (damage function) by colonial competitive algorithm. The performance of the proposed method has been verified using a numerical example, namely a two span steel bridge with and without noise in the modal data and containing one or several damages. The obtained results clearly reveal that the proposed method can be viewed as a powerful and robust method for structural damage detection in structures.