In this paper, an effective joint damage identification method has been proposed. Beam to columns connections modeled as a rotational mass less spring. Joint damage has been presented as a reduction in the connection rigidity or rotational stiffness factor. Because of sensitivity of mode shapes and frequencies on structural stiffness, the first mode shape and frequency of damaged frame has been used as a feature to identify damage in steel frame connections. This data is acquired by the Modal analysis of damaged structure applying the finite element method (FEM). The numerical studies are carried out within the MATLAB (2016) environment, which is used for the solution of finite element problems. To identify joint damage, an optimization problem formulated in which the objective functions formulated based on Modal Assurance Criterion ((MAC)) and (MAC) flexibility. To solve the optimization problem, an effective meta-heuristic called Grey Wolf Optimizer is employed to detect damage in beam to columns connections. To evaluate the performance of presented method, three examples consists of a seven, twelve and fifteen story steel frames are chosen with two different scenarios of damage in beam to columns connections for each of them for this purpose. Results reveal that the proposed approach is effective to detect and estimate damage in steel frame connections.

The health of structures, provision of safety, and the sense of security are among constant requirements and perpetual challenges of engineering and managers in the field of crisis management. Erosion and occurrence of minor local damage to structures and structural members in the early stages of construction or during operation, especially in critical structures such as power plants, tall buildings, stairs, dams, airports, and hospitals, have always been among major problems. As time passes, Structures are affected by a variety of natural and non-natural destructive factors such as earthquakes, non-systematic excavations, dynamic vibrations resulting from explosions and heavy vehicle traffic. In addition, factors such as serviceability expectation beyond the design capacity of structural elements and failure to meet the latest expectations imposed by regulations, use of poor-quality materials and execution problems will reduce efficiency and, consequently the service life of structures. Also, the spread of local damages in structures can impair the overall health of the structure. Undoubtedly, knowledge of structural health and safety is of vital importance and structural health monitoring is recognized as one of the most important subjects that has received a lot of attention from researchers. Plates are one of the most important structural elements that can, when damaged, progressively transfer damages to other elements and lead to overall structural damage incurring irreparable social and economic costs. Due to the increasing applications of steel plates, especially in building structures (as steel plate shear walls) in the present study attempts were made to focus on damage detection and localization as one of the most important steps of health monitoring using Modal dynamic data (natural frequencies and mode shapes) and a proposed diagnostic method based on two-dimensional discrete wavelet analysis. To this end, the modeled steel plate was subjected to frequency analysis in ABAQUS finite element analysis software and the Modal data associated with damaged and non-damaged states were extracted. The results showed differences between the frequencies and lack of correlation between primary and secondary vibration mode shapes based on the Modal Assurance Criterion ((MAC)) and the angle between the primary and secondary mode shape vectors. Using a propoed damage localization index (DLI) based on the wavelet coefficients obtained from the diameter details of the two-dimensional wavelet analysis of the primary and secondary vibration mode shapes, the damage zones were detected by creating a maximum relative jumps in the DLI diagram. Studies showed that DLI values are sensitive to the damage severity of the damage zone and with increasing the damage severity, these values increase in fixed spatial coordinates in the damaged zone. Also, the DLI of one damaged zone is independent of the damage severity of the other damaged zones, and this is a positive advantage in the damage determination process. Otherwise, failure to detect one damaged zone may affect the detection of other damaged zones, and consequently pose problems in the process of damage detection and localization in cases where we are dealing with multiple damage zones. According to the results of the present study, DLI can be proposed as an efficient and effective index in detection and localization of damages in steel plate elements.

Presenting structural damage detection problem as an inverse model-updating approach is one of the well-known methods which can reach to informative features of damages. This paper proposes a model-based method for fault prognosis in engineering structures. A new damage-sensitive cost function is suggested by employing the main concepts of the Modal Assurance Criterion ((MAC)) on the first several modes’ data. Then, Chaotic Imperialist Competitive Algorithm (CICA), a modified version of the original Imperialist Competitive Algorithm (ICA) which has recently been developed for optimal design of complex trusses, is employed for solving the suggested cost function. Finally, the optimal solution of the problem is reported as damage detection results. The efficiency of the proposed method for damage identification is evaluated by studying three numerical examples of structures. Several single and multiple damage patterns are simulated and different number of Modal data are utilized as input data (in noise free and noisy states) for damage detection via suggested method. Moreover, different comparative studies are carried out for evaluating the preference of the suggested method. All the obtained results emphasize the high level of accuracy of the suggested method and introduce it as a viable method for identifying not only damage locations, but also damage severities.

Structural damage detection is one of the primary goals of structural health monitoring. Minimum safety can be provided upon timely identification of the damaged elements and appropriate decisions (repairing or replacing the damaged elements). Today, the use of concrete-filled steel tube composite columns in the construction industry, especially high-rise buildings, is increasing. In these columns, the concrete core debonding from the steel tube is considered a prevalent type of damage. This study discusses the impact of such debonding on dynamic Modal properties (natural frequencies and vibration mode shapes) and the detection of debonding damage area based on wavelet analysis. Debonding to a depth of 3 mm is defined as reduction of concrete stiffness in connection with the steel tube, and the column was subjected to frequency analysis. Modal information, including frequency values and vibration mode shapes, were extracted. Differences in frequency values and Modal Assurance Criterion ((MAC)) smaller than one were observed between primary and secondary shapes of vibrational modes due to the presence of debonded areas. The results showed that with the addition of a new debonding damaged area, the rate of reduction of frequency values increased. The damage index was proposed based on the detail coefficients obtained from discrete wavelet analysis of primary and secondary shapes of vibration modes to identify the area of detachment damage. The results demonstrated that the relative minimum and maximum values of the damage index for all modes occurred in debonding damaged areas. Moreover, the damage index values for different damaged areas were independent of each other. Indeed, the damage index values for other debonding damage situations did not change when a new debonding damaged area was added. This is a positive point in the damage detection process with multiple debonded areas because in this case, the inability to detect a debonding damaged area cannot affect the detection of other debonding damage situations.

Given the crucial role of cable-stayed bridges and the challenging environmental conditions they often face, monitoring their health is essential. Stay cables, which are the primary load-bearing elements in cable-stayed bridges, are prone to damage due to environmental conditions. This paper investigated the performance of a method for evaluating the cables of a cable-stayed bridge using deck responses, compared to Modal parameters. The technique utilized phase space analysis of deck displacement responses in the time domain under a specific moving load. This method was evaluated through a numerical simulation of the Manavgat cable-stayed bridge. Damage scenarios with intensities of 20%, 30%, and 40% were applied to the cross-sections of the cables. Damage identification was carried out by analyzing the changes in phase space topology (CPST) of the responses between the healthy and damaged models, along with two indicators, the Modal Assurance Criterion ((MAC)) and Modal flexibility. The results showed that the CPST index, unlike Modal flexibility, identified the damaged cables at all damage levels except for the back-stay cables. The changes in the Modal Assurance Criterion ((MAC)) at the 40% damage level were also very insignificant. This method can serve as a preliminary approach for fast and continuous monitoring of cables in cable-stayed bridges, minimizing traffic disruption while relying solely on the displacement responses of the deck.

Background: Gamma analysis is an effective tool used to verify treatment plan accuracy with regard to patient specific quality Assurance. In this method, accuracy is validated through the major parameters presented in the acceptance Criterion (AC). The Hybrid AC (HAC) method has been proposed and validated via the Traditional AC (TAC) method of comparison. Materials and Methods: The performance of the HAC method was investigated through one-dimensional (1D) relative dose profile and clinical planar dose distribution. By employing the HAC method, Gamma values were observed at different regions of the profile as well as at the different treatment sites of clinical planar dose distribution. Both results were compared by employing the TAC method, but only planar dose distribution was analyzed by 95% confidence interval of statistics. Results: The results of the HAC method indicate higher Gamma values at the penumbra of the dose profile when compared with the results of the TAC method. In low dose and high dose areas, both methods produced comparable results. In terms of planar dose distribution, the proposed method demonstrated a higher degree of sensitivity than the TAC method by indicating low values for the Gamma passing rate at all treatment sites. Conclusion: The HAC method could effectively increase the sensitivity of the tool at a high dose gradient of planar dose distribution, whereas it had no impact on the area of the low dose gradient. Therefore, this method could be an alternative option for evaluation of treatment planning accuracy in clinical practice.

According to the great importance of safety in aerospace industries, identification of dynamic parameters of related equipment by experimental tests in operating conditions has been in focus. Due to the existence of noise sources in these conditions the probability of fault occurrence may increases. This study investigates the effects of noise in the process of Modal parameters identification by Output only Modal Analysis (OMA) method using Singular Value Decomposition (SVD) algorithm. The study case is the horizontal tailplane of the aircraft; therefore, at first, the Modal parameters of the tailplane are obtained numerically. Then a cantilever beam is used to perform experimental tests with regard to the high aspect ratio of the modeled tailplane. The Modal parameters of the beam are obtained nonparametrically by Experimental Modal Analysis (EMA) and OMA. In order to investigate the effects of noise in a controlled manner, the artificial excitation namely the shaker with the random force is used. Then, the effects of noisy measurements on the specifications of the system in EMA and OMA methods are investigated. The results indicate that: 1. The OMA method has more resistance against the noise for extracting natural frequencies. 2. The results of the Modal Assurance Criterion ((MAC)) values by EMA method, in the condition of noise existence in output data, are worse than the noise existence in input data. 3. The average of (MAC) values in general condition of EMA method by noisy input & output data is worse than the OMA method.

In this paper, the behavior of reinforced concrete beams is studied using an experimental Modal analysis. This analysis was carried out on the test results of nine concrete beam specimens. These beams were loaded gradually in different loading steps until failure. Six of the specimens were strengthened with externally bonded CFRP sheets when load reached approximately half of the predicted failure load. After every loading step, each specimen was suspended by elastic cables for vibration test. The frequency response functions (FRF) of the specimens were obtained in the vibration tests. The eignfrequencies and mode shapes were measured by analyzing the frequency response functions (FRF) and their variations during the loading steps were discussed. The changes of stiffness of the specimens were determined by Modal Assurance Criterion ((MAC)). In some of the specimens strengthened with CFRP sheets, the reduction of eigenfrequencies and the increase of the I-(MAC) values, based on the second vibrating mode shape, were less than 42 and 39 percent from the values associated with the unstrengthened specimen. These values show the effectiveness of the FRP strengthening and agree with the experimental observations.

This paper is concerned with the determination of optimal sensor locations for structural Modal identification in a strap-braced cold formed steel frame based on an improved genetic algorithm (IGA). Six different optimal sensor placement performance indices have been taken as the fitness functions; two based on Modal Assurance Criterion ((MAC)), two based on maximization of the determinant of a Fisher information matrix (FIM), one aim on the maximization of the Modal energy and the last is a combination of two aforementioned indices. The decimal two-dimension array coding method instead of binary coding method is applied to code the solution. Forced mutation operator is applied whenever the identical genes produce via the crossover procedure. An improvement is also introduced to mutation operator of the IGA. A verified computational simulation of a strap-braced cold formed steel frame model has been implemented to demonstrate the effectiveness and application of the proposed method. The obtained optimal sensor placements using IGA are compared with those gained by the conventional methods based on several criteria such as norms of FIM and minimum in off-diagonal terms of (MAC). The results showed that the proposed IGA can provide sensor locations as well as the conventional methods. More important, based on the criteria, four of the six fitness functions, can identify the vibration characteristics of the frame model accurately. It is shown through the example that in comparison with the (MAC)-based performance indices, the use of the FIM-based fitness functions results in more acceptable and reasonable configurations.

In this study, optimal sensor placement (OSP) which plays a key role in the health monitoring of large-scale structures, is investigated using the genetic algorithm (GA). The OSP is among permutation problems that it's challenging to define the crossover operator in this kind of problem. In this study, a new hybrid crossover operator is proposed to find the optimal location for sensors and two different strategies are investigated for selecting members to form the next generation population. Also, the two-structure coding method has been used instead of the typical binary coding method to create the chromosomes of the population members. The objective function and fitness is defined based on the Modal Assurance Criterion ((MAC)) matrix that is calculated with identified mode shapes and analytical mode shapes. The efficiency of the proposed method was investigated on a high-rise structure. The results show that the mode shapes identified by the optimal placement obtained from the proposed method are identical to the analytical mode shapes of the finite element model. Also, the comparison between the sensor locations obtained by conventional operators and the proposed operator shows that the proposed hybrid crossover operator outperforms other operators in terms of accuracy and convergence speed.