Sharif Digital Repository

Triple Module redundancy (TMR) is a commonly used approach to increase reliability in space applications. Applying this method, triple modules and voting circuits are implemented in a field programmable gate array (FPGA). In TMR systems when a single event upset (SEU) occurs, the voting circuit neglects the failure value of a module receiving the SEU and takes a correct value from the other two modules. The present TMR approach utilized for the combinational and sequential logic on the gate level shows that it is possible to write hardware description language (VHDL) code in a structured yet high level coding style to obtain the required redundancy. However using VHDL, the level of required... 

An autonomous method for attitude and position determination of a satellite without using data from ground stations and also without the need for human interference is proposed using the image that the satellite has taken at each moment. The proposed method is based on recognition of stable features in the image and using them in order to match the satellite image with a source image from the Earth and the latitude and longitude of the features become known.SIFT and SURF algorithms have been examined. Using the perspective equation and camera coordinate system, a model has been developed that relates the coordinates of a point in the image with its equivalent coordinates in the real world.... 

In this research, seismic loss of eccentrically braced frame buildings is assessed by using reliability methods. For this end, first, Bayesian linear regression is utilized to characterize the epistemic uncertainty of the problem in the form of damage models. The input of these models are mechanical and physical properties of the components, and the outputs are damage ratios. In the second step, by using the developed models and the comprehensive library of probabilistic models of Rt, reliability analysis is conducted on 4, 8, and 16 stories building located in San Jose. The limit state function is seismic loss, including repair loss, collapse loss, and demolition loss, and analysis method... 

Up till now, various contributions of research to soil-structure-interaction context have been primarily focused on structural systems exhibiting elastic-perfectly plastic material behavior. In this paper, combined effects of material degradation, p-delta, and soil-structure-interaction considering foundation uplift have been deemed to determine seismic response of a single degree of freedom system. The system is composed of a column with a lumped mass on top that is placed on a rigid foundation. The foundation is mounted on Winkler springs and dashpots to account for soil-foundation compliance and material/radiational damping. The springs are tensionless to guarantee that uplift phenomenon... 

In this paper the predictive equations for collapse assessment of shear links used in eccentric braced frames are developed. An extensive database including results of over 70 cyclic tests on steel wide flange shear links is collected and the structural parameters governing the hysteresis behavior are calibrated using a simplified numerical model. The methodology of calibration is to minimize the discrepancy between the experimental hysteresis loops and the corresponding numerical results using Particle Swarm Optimization (PSO) algorithm. The objective function of PSO algorithm is minimized by iterating parameters that govern the hysteresis behavior of the numerical model. Stepwise... 

The purpose of this paper is to quantify the extent of damage of rectangular reinforced concrete shear walls after an earthquake using surface crack patterns. One of the most important tasks after an earthquake is to assess the safety and classify the performance level of buildings. This assessment is usually performed by visual inspection that is prone to significant errors. In this research, an extensive database on the images of damaged rectangular reinforced concrete shear walls is collected from the literature. This database includes more than 200 images from experimental quasi-static cyclic tests. Using the concept of Fractal geometry, several probabilistic models are developed by... 

The aim of this study is introducing and assessing two torsion-based metallic dampers which are named Torsional Disc and Torsional Cylinder dampers based on their geometry and energy absorption mechanisms. As expected, a steel disc and a steel cylinder are exposed to torsion in the Torsional Disc and Torsional Cylinder dampers respectively, and energy absorption occurs through torsional yielding of those two elements. In the introduction section, a mechanism is introduced to put the dampers under pure torsion so as to yield a desirable performance. The dampers are designed to be placed between Chevron braces and the floor beam, and the pure torsion is exerted, through the mentioned... 

This study investigates the effect of the variation of the properties of unreinforced brick masonry walls, such as aspect ratio, prism strength, and axial load ratio, on determining the post-earthquake damage states of the walls using fragility curves. A database comprising 245 damaged unreinforced masonry walls is collected that have been tested under the quasi-static cyclic loads up to more than three percent drift ratios. The collected walls are categorized into four damage states according to the resisting lateral force during the cyclic loading obtained from the backbone of the hysteresis curves. Then, multivariable fragility surfaces based on the drift ratio and design properties of... 

This thesis introduces an interpretable data-driven approach to reconstructing the complete backbone curve of unreinforced masonry (URM) walls using the structural properties as inputs. The framework utilizes supervised and unsupervised learning combined with autoencoder neural networks. The dataset used to develop the predictive model includes crack texture images, design, and mechanical properties, as well as the backbone curves associated with 165 URM walls subjected to quasi-static cyclic loading. Most of the walls in the experimental database experienced hybrid failure modes. Therefore, autoencoder networks are used to extract informative features from the backbone curves, which are... 

This study investigates the seismic behavior of eccentrically braced frames (EBFs) taking into account the influence of masonry infill walls. The primary objectives of the study are to predict the seismic global response and develop associated fragility curves using a nonmodel scenario-based machine learning framework. To model the infill walls, equivalent diagonal struts are employed, and a nonlinear pushover analysis is conducted to assess the overall impact of infills on 4- and 8-story EBF structures. An extensive database of 4 bare and 48 infilled EBFs with various infill properties is assembled to predict story-based engineering demand parameters (EDPs) containing peak and residual... 

The aim of this research is to predict the backbone curve of reinforced concrete shear walls by identifying different failure modes. Failure mode identification is performed by measuring the contribution percentage of flexure, shear, and combined flexure-shear failure modes in the force-displacement response of the wall under cyclic loading. The approach used in this research is a probabilistic and data-driven method, which leads to the calculation of model uncertainty in detecting the wall's failure mode and, consequently, the prediction uncertainty of the backbone curve. The database used in this research consists of 253 reinforced concrete shear walls, obtained from a review of existing... 

Damage detection of civil structures and infrastructures using partial modal information has been an interesting field of research. So far, three main algorithms are introduced for health monitoring of structures. The first method relies on identifying stiffness and mass properties, as physical parameters, of structures using inverse eigenvalue problem considering mass modifications in 2D and 3D shear-frame models of structures. Comparing the identified matrices with the primary values, one can detect the damage occurrence, its level and severity in the structure. Applicability of this method in health monitoring of offshore jacket platforms as one of noteworthy civil infrastructures has... 

This research presents advanced methodologies for the seismic damage assessment of structures, focusing on the rapid and precise estimation of key engineering demand parameters. By integrating modern signal processing techniques with machine learning models, the study introduces a novel, data-driven framework that eliminates the reliance on traditional, computationally intensive modeling approaches. Leveraging real-time data from seismic sensors, these methodologies accurately predict critical performance metrics including residual and peak story drift ratios, peak floor accelerations, and fragility curves, significantly improving the accuracy of seismic assessments. The inclusion of... 

This paper presents a new approach for developing multivariable fragility curves, which examines the effects of the variation of multiple design parameters on the probability of reaching different damage states. The database of this study comprises the results of 260 RC columns tested under the quasi-static cyclic loading. The push of the cyclic plots, technically named the backbone curve, is also extracted for the entire database by the authors. Accordingly, the RC columns are classified into four levels of damage based on the resisting lateral force observed throughout cyclic loading, derived from the backbone of the hysteresis curves. Considering the drift ratio as the primary variable... 

Although manual crack inspection has been widely used for structural health monitoring over the last decades, the development of computer vision methods allows continuous monitoring and compensates the human judgment inaccuracy. In this study, an image-based method entitled, Arc Length method is introduced for extracting crack pattern characteristics, including crack width, and crack length. The method contains two major steps; in the first step, the crack zones are estimated in the whole image. Afterwards, the algorithm finds the start point, follows the crack pattern, and measures the crack features, such as crack width, crack length, and crack pattern angle. The efficiency of the method... 

This study presents a Machine Learning framework for post-earthquake structural damage state and economic loss estimation. The framework is proposed to rapidly determine whether a structure is safe or unsafe to be occupied following a seismic event, to predict the detailed structural response, and finally to evaluate the economic loss of the buildings. An extensive database is developed by analyzing twelve low-rise, mid-rise, and high-rise eccentrically braced frames (EBFs) under 44 scaled ground motion records through incremental dynamic analysis (IDA) to train the Machine Learning algorithms. Machine Learning algorithms are employed to classify the structural damage state of the buildings... 

This research proposes a damage assessment framework based on the visual features of a damaged reinforced concrete shear wall, such as crack pattern distribution, crushing areal density, aspect ratio, and the presence of the boundary condition. The study contains two parts: identifying the performance level of the damaged walls (i.e., Immediate Occupancy, Life Safety, and Collapse Prevention) and estimating the residual strength and drift ratio of the walls. The research database contains 236 images of 72 reinforced concrete shear walls tested in the laboratory under the quasi-static cyclic loadings at various drift ratios between 0.0 to 4.0. To identify the performance level of a damaged...