Makale Koleksiyonu
https://hdl.handle.net/11491/2133
Article Colleciton2024-03-28T17:19:01ZDetermination of Natural Fundamental Period of Minarets by Using Artificial Neural Network and Assess the Impact of Different Materials on Their Seismic Vulnerability
https://hdl.handle.net/11491/8731
Determination of Natural Fundamental Period of Minarets by Using Artificial Neural Network and Assess the Impact of Different Materials on Their Seismic Vulnerability
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.
2023-01-01T00:00:00ZA Hybrid Artificial Neural Network-Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings
https://hdl.handle.net/11491/8726
A Hybrid Artificial Neural Network-Particle Swarm Optimization Algorithm Model for the Determination of Target Displacements in Mid-Rise Regular Reinforced-Concrete Buildings
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.
2023-01-01T00:00:00ZCorrigendum for “Polymer-graphene hybrid stabilized ruthenium nanocatalysts for the dimethylamine-borane dehydrogenation at ambient conditions” [Journal of Molecular Liquids Volume 279, 1 April 2019, Pages 578-583; 10.1016/j.molliq.2019.02.003]
https://hdl.handle.net/11491/6178
Corrigendum for “Polymer-graphene hybrid stabilized ruthenium nanocatalysts for the dimethylamine-borane dehydrogenation at ambient conditions” [Journal of Molecular Liquids Volume 279, 1 April 2019, Pages 578-583; 10.1016/j.molliq.2019.02.003]
Şen, Betül; Aygün, Ayşenur; Şavk, Aysun; Duman, Sibel; Çalımlı, Mehmet Harbi; Bulut, Ela; Şen, Fatih
[No abstract available]
2021-01-01T00:00:00ZObservation of behavior of the Ahlat Gravestones (TURKEY) at seismic risk and their recognition by QR code
https://hdl.handle.net/11491/5646
Observation of behavior of the Ahlat Gravestones (TURKEY) at seismic risk and their recognition by QR code
Işık, Ercan; Antep, Barış; Büyüksaraç, Aydın; Işık, Mehmet Fatih
Protection of cultural heritage and carrying it to the future are at the top of the significant topics of research and implementation in engineering in the 21st century. There are several historical structures in the district of Ahlat located in the east of Turkey on the Lake Van Basin that has harbored many civilizations. Some of such works are the gravestones that are found in the Ahlat Seljuk Cemetery, which is the oldest and largest cemetery in the district. This study firstly provides information about the Ahlat Seljuk Cemetery and the gravestones found in it. Observation-based structural analyses were carried out on these gravestones that are found in this area that are known to have belonged to different civilizations based on their physical and constructional characteristics. These stones were built out of Ahlat stone as single pieces. Information is provided on the damages that have occurred on the gravestones in time and their causes. In general, losses of mass, abrasions, separations, collapses and calcifications due to natural conditions, as well as vegetative formations, were observed in the gravestones. To provide an example of other gravestones within the context of the study, the gravestone that is known to belong to the person named Nureddin Ebu Hasan was selected. As a result of the modeling that was carried out for this gravestone by using the finite elements method, modal analyses were carried out. With these analyses, for the gravestone, period, effective mass participation rates and stress values were calculated. The stress values that were obtained in this study were compared to the material safety stress values that were obtained in previous studies. Additionally, QR code application was created for the gravestone that was selected as an example in the study, and information on this gravestone was transferred to an electronic environment. The QR code application includes different language options, visuals of the gravestone and information on the gravestone. The QR application was also supported with a video of the cemetery where the gravestone is located. With this application, access to information about gravestones will be possible by using tablets and smartphones. With a QR code to be created for each gravestone, these gravestones will obtain identity cards.
2019-01-01T00:00:00Z