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    A Hybrid Artificial Neural Network-Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings
    (MDPI, 2023) Işık, Mehmet Fatih; Avcil, Fatih; Harirchian,Ehsan; Akif Bülbül, Mehmet; Hadzima-Nyarko, Marijana; Işık, Ercan; İzol, Rabia; Radu, Dorin
    Abstract: The realistic determination of damage estimation and building performance depends on target displacements in performance-based earthquake engineering. In this study, target displacements were obtained by performing pushover analysis for a sample reinforced-concrete building model, taking into account 60 different peak ground accelerations for each of the five different stories. Three different target displacements were obtained for damage estimation, such as damage limitation (DL), significant damage (SD), and near collapse (NC), obtained for each peak ground acceleration for five different numbers of stories, respectively. It aims to develop an artificial neural network (ANN)-based sustainable model to predict target displacements under different seismic risks for mid-rise regular reinforced-concrete buildings, which make up a large part of the existing building stock, using all the data obtained. For this purpose, a hybrid structure was established with the particle swarm optimization algorithm (PSO), and the network structure’s hyper parameters were optimized. Three different hybrid models were created in order to predict the target displacements most successfully. It was found that the ANN established with particles with the best position revealed by the hybrid models produced successful results in the calculation of the performance score. The created hybrid models produced 99% successful results in DL estimation, 99% in SD estimation, and 99% in NC estimation in determining target displacements in mid-rise regular reinforced-concrete buildings. The hybrid model also revealed which parameters should be used in ANN for estimating target displacements under different seismic risks.
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    Determination of Natural Fundamental Period of Minarets by Using Artificial Neural Network and Assess the Impact of Different Materials on Their Seismic Vulnerability
    (MDPI, 2023) Işık, Ercan; Ademovic, Naida; Harirchian, Ehsan; Avcil, Fatih; Büyüksaraç, Aydın; Hadzima-Nyarko, Marijana; Bülbül, Mehmet Akif; Işık, Mehmet Fatih; Antep, Barış
    Abstract: Minarets are slender and tall structures that are built from different types of materials. Modern materials are also starting to be used in such structures with the recent developments in material technology. The seismic vulnerability and dynamic behavior of minarets can vary, depending on the material characteristics. Within this study’s scope, thirteen different material types used in minarets in Türkiye were chosen as variables. A sample minaret model was chosen as an example with nine different heights to reveal how material characteristic change affects seismic and dynamic behavior. Information and mechanical characteristics were given for all the material types. Natural fundamental periods, displacements, and base shear forces were attained from structural analyses for each selected material. The empirical period formula for each material is proposed using the obtained periods, depending on the different minaret heights taken into consideration. At the same time, fundamental natural periods for the first ten modes and 13 different types of materials used in the study were estimated with the established Artificial Neural Network (ANN) model. The real periods from the experimental analyses were compared with the values estimated by the ANN using fewer parameters, and 99% of the results were successful. In addition, time history analyses were used to evaluate the seismic performance of the minaret (three different materials were considered). In this specific case, the acceleration record from the 2011 Van (Eastern Turkiye) earthquake (Mw = 7.2) was taken into consideration. Performance levels were determined for the minaret according to the results obtained for each material. It has been concluded that material characteristics significantly affect the dynamic and seismic behavior of the minarets.