In this paper, an alternative approach in operational modal analysis is presented, utilizing image processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

One of the numerous methods recently employed to study the health of structures is the identification of anomaly in data obtained for the condition of the structure, e. g. the frequencies for the Structural modes, stress, strain, displacement, speed, and acceleration) which are obtained and stored by various sensors. The methods of identification applied for anomalies attempt to discover and recognize patterns governing data which run in sharp contrast to the statistical population. In the case of data obtained from sensors, data appearing in contrast to others, i. e. outliers, may signal the occurrence of damage in the structure. The present research aims to employ computer algorithms to identify Structural defects based on data gathered by sensors indicating Structural conditions. The present research investigates the performance of various methods including Artificial Neural Networks (ANN), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Manhattan Distance, Curve Fitting, and Box Plot in the identification of samples from damages in a case study using frequency values related to a cable-support bridge. Subsequent to the implementation of the methods in the datasets, it was shown that the ANN provided the optimal performance.

Stochastic subspace identification (SSI) is a process that linearizes the identification problem by utilizing singular value decomposition (SVD) and QR factorization. This technique enables the extraction of system matrices through linear least squares. However, the estimated systems in these methods are affected by the user-defined dimensions of the data space (Hankel matrix). Also, SSI does not explicitly minimize a cost function for estimating system matrices, making statistical analysis difficult. To enhance the accuracy of modal specifications obtained from SSI, especially the damping ratios, this research suggests using output-error methods (OEM). During OEM, the process involves iteratively adjusting the model parameters to match the outputs of the simulated model with those of the observed system. The following steps are taken to enhance the OEM for extracting Structural properties: Firstly, the initial term is derived using the SSI to reduce the number of optimization iterations. Secondly, by using the Gauss-Newton approach, the nonlinearity of the objective function is reduced by treating the second-order derivatives as a linear system. Finally, Gradient project minimization is utilized in SSI to ensure the injectivity of estimated systems. The OEM was validated by analyzing the response of a 3-DOF excited by white noise with an SNR of 1 db.  Then, the model was then applied to seismic observations of Pacoima Dam during the 2001 San Fernando and 2008 Chino Hills earthquakes. The two main modes of the structure were extracted, and they had the least error compared to the developed finite element models.

The objective of the current work is to show the effectiveness of using wavelet transform for detection and localization of small damages. The spatial data used here are the rotational mode shapes of the damaged and undamaged plate-like structures. The continuous wavelet transform using complex Gaussian wavelet is used to get the spatially distributed wavelet coefficients so as to identify the damage position on a square plate. The rotational mode shape data of the square plate with damage of different sizes are obtained using ANSYS 9.0. Damage identification for different boundary conditions is studied.

Identifying the constraining factors of production and yield gap is very important. Therefore; this research was performed to identify the production constraining factors of local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the actual and calculated potential yield were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The yield gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total yield gap. The yield gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total yield variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the yield gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, actual yield mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha yield gap . Mean relative yield and relative yield gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each restricting yield variables.

Structural and crack parameters in a continuous mass model are identified using Observer Kalman filter identification (OKID) and Eigen Realization Algorithm (ERA). Markov parameters are extracted from the input and out responses from which the state space model of the Structural system is determined using Hankel matrix and singular value decomposition by Eigen Realization algorithm. The Structural parameters are identified from the state space model. This method is applied to a lumped mass system and a cantilever which are excited with a harmonic excitation at its free end and the acceleration responses at all nodes are measured. The stiffness and damping parameters are identified from the extracted matrices using Newton-Raphson method on the structure. Later, cracks are introduced in the cantilever and all Structural parameters are assumed as known priori, the unknown crack parameters such as normalized crack depth and its location are identified using OKID/ERA. The parameters extracted by using this algorithm are compared with other Structural identification methods available in the literature. The main advantage of this algorithm is good accuracy of identified Structural parameters.

The paper concerns with the identification of the surface and subsurface defects based on Lamb wave generation and propagation in a structure with piezoelectric wafer active sensor. The piezoelectric wafer acts as an actuator at the initial instant for Lamb wave generation and then as sensor to monitor Lamb waves reflected from different surfaces of the structure. A finite element model is developed to analyze the response of the structure and piezoelectric wafer subjected to initial activation signal. The model simulates the generation of Lamb waves which are divided into symmetric and anti-symmetric waves propagating with different velocities. The reflection of Lamb waves from exterior surfaces of the structure or the surfaces of the defects are identified using the present model. The performance of the damage identification procedure is evaluated for a plate structure with single or multiple transverse grooves whose dimensions are very small compared to Structural dimensions in order to simulate the transverse cracks in the structure. The results have shown that Lamb waves are very sensitive to transverse grooves and determine the location of transverse damages with a reasonable accuracy.

Output-only Structural identification is conducted by output data of the structure. These data usually include Structural response together with some noise. Success of output-only methods in determining the vibration parameters of a structure depends on the signal to noise ratio (SNR) of the output data. In this paper, the vibration parameters (Natural frequency and Mode shape) of a contilever beam have been obtained using output data which have different signal to noise ratios. The vibration parameters of the beam were determined using modal analysis of finite element model and considered as reference parameters. Then, appropriate input was applied to the beam and the acceleration signal was obtained. To generate noisy data, noise with different powers compared to signal powers were added to acceleration signal. The modal parameters of the beam were obtained using two output-only methods, Peak Picking (PP) and Stochastic Subspace identification (SSI). The vibration parameters having signal-to-noise ratios greater than 25 (lower noise level) for all considered modes were identified properly. At a signal-to-noise ratio of 0. 25 to 25 (higher noise level), it was not possible to identify the modal parameters of the first mode of the beam, but the parameters of the higher modes were identified with good accuracy.

In this paper dynamic damping properties of a nominated flexible Structural adhesive have been identified using an extended-direct modal based joint identification method. It has been revealed that damping characteristics of adhesive are correlated to both frequency and mode shape. Young’s and shear moduli increase with frequency but damping on the other hand, decrease. The results showed that mode shape has an important role on the dynamic mechanical properties of adhesive. Modes that dominantly create normal stress on the adhesion surface represent higher stiffness and lower loss factor compared to shear modes. The different level of loss factors between bending modes and shear modes are notable, but damping property of bending modes and shear modes on the other hand, is in the same order and decreases with frequency. It has been shown that the effective, viscoelastic, mechanical properties of the adhesive can be identified successfully, using the suggested method of identification.

Competition law is a newcomer to the legal system recently. A sound understanding of competition policy can provide us with sufficient bases to apply a fundamental and normative view of the issues of competition law. The difference in supervision and regulation determines how the market functions and in order to understand this difference one must understand competition policy. Competition policy may be based on governmental support for national production and industry or on a non-interventional and regulatory posture. Moreover, supervision, based on the principle of non-intervention in the market mechanism, is rooted in liberal ideas; however, regulation, whether as a rule or an exception, is based on the assertion that the market has been ineffective in attaining its goals. Therefore, the government will resort to interventions to regulate inefficiencies.  This paper aims to analyze Supervisory Authority in Implementing Competitive Policy by employing the description method. In this article the author tries to first delineate competition policy, its related requirements and imposed deviations to the market. Then, by defining the supervisory entity and clarifying its distinction from the regulatory institutions, the author considers the characteristics of an appropriate supervisory entity conducting a comparative study of this issue in Iran and the U.S.A. This form of Competition policy because of its applicable experiences which have been well described by recent scholarship is considered suitable for the native system.