Recently, identification of dynamic properties of structures such as bridges, towers and building, which are not easy to excite, is possible by measuring only the output responses. Due to measuring only the responses when the structure is excited only by ambient or operational forces, these methods are referred as output-only modal analysis or operational modal analysis (OMA). One drawback of OMA is that the MODE SHAPES are not scaled. However the scaled MODE SHAPES are required for some important applications such as damage detection or MODEl updating. In this paper, a procedure is proposed for precise scaling of the operational MODE SHAPES. The method is based on the sensitivity analysis of the effective parameters in the accuracy of scaling. The finite element MODEl of a ten storey building is used to present the capability of the method. The natural frequencies and unscaled MODE SHAPES have been estimated using FDD method and the mass change method is applied for scaling the MODE SHAPES. The sensitivity of the results to the amount, location and the number of added masses and the number of MODEs under investigation have been evaluated. It is shown that the amount of added masses can be estimated by try and error of the FE MODEl of structure and the masses should be distributed all over the structure to decrease the scaling error.

Damage identification from changes in vibration characteristics of structures is the subject of many current researches. In this regard, several different methods, Le. vibration frequency changes, MODE shape changes, strain energy MODE shape changes, and the wavelet analysis, have been suggested. In this paper, these methods are applied to a rectangular cracked plate. The vibrational characteristics of the plate are obtained, and the results for different locations and sizes of the crack are studied. This comparison shows that the method of vibrational frequency changes has the least sensitivity to the presence of the damage, while the methods of the damage index changes and the wavelet analysis are most sensitive to the damage. The damage detection in all of the above mentioned methods except the wavelet analysis is done through detection of any changes in the vibrational characteristics before and after the introduction of the damage.

Currently there are just a few papers about fault detection of tubes and cylindrical shells. Recently, methods based on modal curvatures have gained great attention due to their sensitivity to defects. But the methods require dense number of data extraction points which limits their industrial application. In this research a new method for damage identification of cylindrical shells has been introduced. The method does not need intact structure's data and in contrast to other methods requires a few data extraction points. By transferring the modal information of the structure to the ANN, output of the network is exact position of the defect on the structure and the method does not need skilled technician to interpret the data. Performance of the network is validated by unfamiliar data for the network and 0. 97 regression is obtained.

Thin wall shell structure has low weight and high resistance. The load capacity, buckling behavior and post buckling behavior of steel tanks thin wall, is very sensitive to geometric imperfections. Due to the small wall thickness of the shell structures enabling the creation of any deformation and there is a disturbance on the surface of the wall. Considering the types of errors occurred when build or assembled these structures, are not built these structure, ideally.This imperfections may be in the process of rolling, removable panels, installation or welding arise. Incomplete reports about the negative impact of the effect of welding on the axial bearing capacity.Comprehensive research on the effects of imperfection of initial geometrical shape on the steel tank vibration MODEs, and its effect on the bearing capacity steel storage tanks that considerable research It's not done. In this research, the actual behavior of cylindrical shells with initial geometric imperfections on MODE SHAPES steel tanks in the pre-buckling and post-buckling. And the effect of initial geometric imperfections on steel tanks slashing been paid. Using finite element software, ABAQUS, and verification of the results of the analysis and nonlinear analysis with experimental results. Been paid. Imperfect geometric shape has changed mods. The effect of these changes on slashing are very small.

Much attention has been given to structural damage detection in recent decades in order to assess the reliability of structures during their service time. To detect damage in structures, one method, among different ones, is considered the most important i.e. the vibration-based methods. Because the modal parameters of structures like frequency and MODE shape are so sensitive to structural properties like stiffness, it can therefore be used for detecting damage in structures. This paper presents a novel approach for structural damage detection and estimation using expanded MODE SHAPES and extreme learning machine (ELM). One of the problems in damage detection is the compatibility between the number of sensors and Degree of Freedoms (DOFs) in the finite element MODEl of structures, in which the number of sensors, installed to structure, is usually less than the number of DOFs in the finite element MODEl. So, the MODEl reduction method should be used to match incomplete measured MODE SHAPES or the measured MODE SHAPES should be expanded to the dimension of the analytical MODE SHAPES. In this study, the second option is adopted, using the improved reduction system (IRS) transformation matrix and used as input parameters to the ELM for damage identification. The proposed method uses the first two expanded MODE SHAPES and natural frequencies as the input parameters and damage states as output to train the ELM MODEl. Also, noise effect on the measured modal data has been investigated. The present method is applied to three examples consisting of a four span continuous beam, plane steel truss and four story plane frame. The obtained results demonstrated the accuracy and efficiency of the proposed method using incomplete modal data. Also, the results obtained indicate that the proposed method is a promising procedure for damage identification in spite of use of noisy modal data.

Non-destructive damage detection methods analyze the output data collected from sensors to track the changes in the dynamic characteristics of the structure and detect the occurrence of damages. continuous recording and analysis of data to be aware of its safety and serviceability requires a network of sensors that are selected optimally and intelligently. Saving the cost of equipping the structure with this optimal sensor network, along with reducing damage detection error, has turned the issue of selecting the number and location of sensors into an optimization problem from an economic and functional point of view. MODEl order reduction methods along with optimization tools can play an effective role in selecting the master degrees of freedom. These methods divide the degrees of freedom of the structure into two groups of master and slave degrees of freedom. The master degrees of freedom appear in the process of calculating the MODE SHAPES and natural frequencies, and the slave degrees of freedom are excluded from the equations. Finally, using the transfer matrix, the complete MODE SHAPES are calculated using the MODE SHAPES of the master degrees of freedom. In this paper, considering the key role of modal parameter recognition in structural damage detection, the performance and accuracy of different methods of dynamical MODEl order reduction in the optimal sensor placement problem was studied. The 2d truss stucture and two-dimensional shear frame are MODEled and analyzed. The sensor placement should be considered in such a way that the MODE shape identification is done with sufficient accuracy and proper recognition. One of the effective tools in order to achieve this goal is to use the capabilities of metaheuristic optimization algorithms along with the capability of dynamic MODEl reduction methods in the stage of identifying the MODE SHAPES and before identifying the damages of structure. Combining MODEl order reduction methods with metaheuristic optimization algorithms so that the selection of appropriate degrees of freedom for sensor installation (master degrees of freedom) leads to the most accurate identification of structural MODEs SHAPES is one of the main objectives of this study. The objective functions selected based on modal assurance criteria (MAC) and Fisher information matrix (FIM) and the capabilities of multi objective particle swarm optimization algorithm (MOPSO) to achieve the optimal number and proper arrangement of sensors are used to better identify the structural MODE SHAPES and proper arrangement of sensors and obtained for system identification purposes. The results report better performance of SEREP and IDC methods in selection of master degrees of freedom and identifying the MODE SHAPES of 2d truss and shear frame structures. According to the MODEling and analysis performed for optimal placement of sensors using different MODEl reduction methods, it can be concluded that the improved dynamic condensation (IDC) method is more accurate than other methods in identifying shear frame MODE SHAPES and gives a smaller maximum non-diagonal MAC matrix element. Also, as the number of sensors increases due to the addition of information to the Fisher matrix, the Fisher matrix determinant increases and second objective function decreases. On the other hand, by reducing the number of available sensors, a limited number of MODEs can be detected. In this case, the best way to receive the structural modal information would be to place more available sensors on the lower and upper floors of the shear frame. Eventually, it can be concluded that the use of IDC and SEREP methods to select master degrees of freedom for sensor installation leads to better identification of modal parameters of the structure. Therefore, the capabilities of these methods can be used to identify damage in structures with a limited number of sensors.

The present study aims to propose a robust method to detect the damage severity and location of the structural elements, focusing on the data type and acquisition method and promoting the MODEl updating tools. The novelty of this method lies in its rotational MODE shape acquisition that provides valuable information on the damage. In this method, the damaged elements were indirectly identified by detecting the healthy elements and eliminating them from the search space. Moreover, this method could minimize the modal strain energy difference between the damaged reference MODEl and the numerical MODEl using an optimization algorithm. An improved genetic algorithm was then employed to perform the optimization task. In this study, four numerical and two experimental damage scenarios were applied to a simply supported beam to examine the performance of the proposed method. Data acquisition systems were implemented using vision-based and accelerometer-based methods. The results indicated that this method could accurately identify the location and severity of damage using only the first MODE shape since the rotational MODE SHAPES were more sensitive to damage than the vertical MODE SHAPES.

Damage in structural elements causes obvious changes in their physical properties such as stiffness and damping. These changes affect the stiffness and damping matrices of whole building so its MODE SHAPES change. Therefore, MODE SHAPES of existing building are widely used in damage detection methods. Since vibration test can only provide translational components of MODE SHAPES, previous methods mostly worked with this type of data. This paper focused on considering the importance of using some rotational/translational components of the MODE SHAPES to detect damages in structural frames. In order to analyze the frames and update them, an automatic iterative MODEl updating program is developed in MATLAB software that works with OpenSees for conducting finite element analysis. The iterative program evaluates a set of objective functions in each step and tries to optimize them by means of nonlinear least square method. Objective functions are defined based on the combination of two criteria of these four items: comparison between frequencies and/or MODE SHAPES of two situations, the modal assurance criteria (MAC), and the modal flexibility matrices. In each step of the analysis, based on optimization results, a new frame will be MODEled in OpenSees software that its elements stiffness is changed according to new sets of data, then finite element analysis will be done and new modal data will be extracted and optimization process will be repeated by new data. To verify the effectiveness of the developed program, two three-dimensional steel structures are MODEled and evaluated, one of them is a five-story moment resisting frame and the other one is a three-story brace frame. It has been considered that these frames suffered damages which are defined by three different scenarios for each of them. Damage scenarios consist of minor, severe and both minor and severe damages. Actually, in this study, damages are defined by reduction in elements’ stiffness. In fact, damage is a percentage of reduction of stiffness in damaged element in comparison with its healthy condition. MODE shape components and natural frequencies of damaged structures are the only needed input data for the program. To investigate the influence of rotational components in MODEl updating, frames have been analyzed with three types of data in each scenario, all translational or rotational components, and all components of MODE SHAPES. Extensive analyses show that among employed objective function, the one which compares MODE SHAPES is the most successful one in damage detection, also modal flexibility can be effective when it works by only rotational components of MODE SHAPES. The findings indicated that the translational components of MODE SHAPES are not capable of detecting damages accurately. Results of MODEl updating by use of only translational components of MODE SHAPES indicate that not only the damages’ location and their intensities could not be predicted, but also several false damages are reported in undamaged elements. It can be concluded that using rotational data leads to more precise results in determining both damages’ locations and their intensities. Besides, the number of false damage detection has been decreased by use of rotational components. It means, when the rotational components are employed, the methods report no damage in healthy elements or the amount of detected damage is very small that can be ignored. Real data extracted from existing building are always polluted by noises due to human or machine faults or sometimes errors in numerical methods lead to inexact input data. Since the data employed in this study are exact numerical data, to consider the effects of these errors, analytical modal data has been polluted by some noises. These noises are generated by use of random function in MATLAB software. Surprisingly, the results show that even with noisy data, the proposed method can detect damages precisely.