جستجوی مقالات مرتبط با کلیدواژه "شاخص انعطاف پذیری مودال" در نشریات گروه "عمران"

Civil structures inevitably undergo damage over time due to various reasons such as environmental changes, material aging, load variations, and insufficient maintenance. Monitoring these structures, especially aging ones, is crucial to detect damage early on and implement suitable retrofitting measures, ensuring their continued safe and reliable operation without unexpected failures. Consequently, there has been significant research in this field, focusing on damage detection in both simple and complex structures. Health monitoring of highway bridges is essential for achieving a reliable transportation system. The vibration-based damage detection method uses changes in the vibrational properties of structures to detect damages and ensure a healthy state. In this study, the absolute value of the modal flexibility damage index and the modal strain energy damage index simultaneously are utilized to prevent unsafe decisions. These absolute values of modal strain energy and flexibility damage indexes are utilized as the bases for training deep neural networks (DNNs). These indexes are applied to provide safe decisions and reliable damage evaluation in steel girder of the highway bridges. The convolution neural network (CNN) is utilized for damage quantification estimation. The CNN is one of the deep learning models that can currently be applied in 2D dominant approaches, such as pattern recognition and speech recognition. In addition, these networks can utilize the 1D time domain and vibrational signal data via the convolutional layer. The initial stage of CNN model comprises combined convolutional and pooling layers that apply different filters to extract features. Following this, fully connected layers, similar to a hidden layer of a multilayer perceptron are incorporated. Ultimately, these layers are classified together with a softmax layer. The convolution layer acts as a filter that convolutes the input layer with a set of weights, adding bias and applying an activation function to the outcome. Gradient descent momentum methods (SGDM) can be employed to optimize the parameters in CNN network architecture. SGDM estimates the gradient with high velocity in any dimension. This method mitigates issues such as jittering and saddle points by utilizing high-velocity inconsistent gradient dimensions and the SGD gradients, respectively. Additionally, when the Current gradient approaches zero, the SGDM provides some momentum. The convolution neural network is trained to utilize damage indexes obtained from numerical simulation of the validated finite element model of the bridge. The damage indexes as the inputs for the neural network, which are achieved from different damage scenarios. Once network training and validation are completed, a well-trained neural network is used to detect, localize, and quantify the intensity of unknown damages. The proposed method overcomes previous damage detection problems such as false positive indications, the unreliability of a single damage index, and insufficient precision in determining the intensity.  The results revealed that the presented method, based on the dual updated damage indexes and CNN, practically and accurately identified unspecified single damages' location and severity in multi-span beams. The new training method of deep neural network systems overcomes some shortcomings in ANN. Moreever, this deep neural network training scheme can reduce the need for huge amounts of input data and enhance the accuracy of network training. The method is capable in predicting single damage scenarios in steel beam.

In this study, damage detection methods of cable stayed bridges were investigated. Cable stayed bridges are flexible structures; meanwhile they are sensitive to vibrations due to their complicated and multiple vibrations modes; therefore, damage detection methods based on vibration data in cable-stayed bridges has become a challenging issue. In the present study, finite element model of Bill Emerson, Missouri cable stayed bridge was simulated in order to achieve a precise finite element model to simulate the damage scenarios in bridge and the study of them. General process includes four damage detection indices based on the modal data (mode shapes and natural frequencies) achieved by modelling structure and simulated damages and in each case the results of damage detection were presented by indices. These methods are: Enhanced Coordinate Modal Assurance Criterion (ECOMAC), Mode Shape Curvature (MSC), Modal Flexibility Index (MFI), Damage Index (DI). Some of the methods were applied in damage detection of the pervious structures and bridges. In this paper, correlative study of these methods were performed based on different damage scenarios as well as study of challenges such as different levels of random noise in the input data, incomplete modal data and low damage intensity in detection of damage in cable-stayed bridge and then, performance of the methods were assessed.