alireza tavana

Today explosion in urban centers and residential areas, there is a danger that threatens all buildings. Explosions inside or near a building can cause sudden damage to building frames. Analysis and design of structures under explosive loads requires a precise understanding of the dynamic response of members and systems of structures under this load.In this research, the behavior of three types of flexural joints of reduced type, end plate and reinforced with top and bottom plate in the phenomenon of explosion and progressive failure has been studied and to perform these studies and numerical simulation of ABAQUS finite element software And Etabs (V.NL13) were used.In determining the type of explosion, two parameters are usually decisive: the size of the weapon, which is equivalent to the weight of TNT, and the distance, which indicates the distance from the center of the explosive to the desired volume. In steel structures, there are two types of lateral load-bearing systems: flexural frame system and simple frame system with bracing. In the meantime, the bending joints of the beam to the column are the bridge of this partnership, and if it is destroyed, the structure can suffer a lot of damage. Due to the above explanations and the importance of flexural joints under lateral loads, in this research, flexural joints have been investigated.

The development of structural health monitoring algorithms for wind turbines is an emerging need because of the aging issue in wind farm facilities. An emerging field of data-driven machine learning schemes has resulted in the development of new means in structural health monitoring. Although, these approaches are inclined to errors in the absence of good insight into the physics of the system. Therefore, a comprehensive model of the structure, as well as its uncertainties, could be a good complement to these approaches. In the current article, an algorithm is developed for autonomous health monitoring of a wind turbine blade, which is one of the most expensive parts of the turbine, based on acceleration measurements taken from several points on the blade. The data are acquired based on a close-to-reality finite element model of the blade. The acceleration signals are gathered from five nodes along with the wind turbine model, which act as vibration sensors in a common similar test setup. Advanced algorithms of system identification are used for extracting damage sensitive features. Moreover, a one-class kernel support vector machine is trained to find the data associated with a damaged state of the structure. Finally, the success of the procedure in the detection of the existence and location of the damage is depicted.