GCRIS Repository Collection:
https://hdl.handle.net/20.500.13091/4
2024-03-29T08:16:33ZAnalysis of Publications on Earthquake Research in Architecture Category and Analysis with R Studio-Biblioshiny Software
https://hdl.handle.net/20.500.13091/5244
Title: Analysis of Publications on Earthquake Research in Architecture Category and Analysis with R Studio-Biblioshiny Software
Authors: Karataş, Lale; Dal, Murat; Barkut, Emine Banu
Abstract: The purpose of this research is to examine the publications focusing on earthquakes in the category of architecture (web of science). The data of the research was analyzed with the Biblioshiny software program. This software program makes a bibliometric analysis on which topics and concepts earthquake research focuses. In addition, images and frequencies of publications related to architecture and earthquakes were revealed. The data of the research was collected between 1-15 July 2023. Results for Architecture (WoS Categories) AND earthquake* (Topic) OR earthquake AND architecture (Topic) OR earthquake AND house (Topic) OR earthquake AND structure (Topic) OR earthquake AND damage (Topic) OR earthquake AND city (Topic) OR earthquake AND urban (Topic) and Türkiye (Countries/Regions). Data were collected with keywords in the Web of Science database. According to the research findings, there are 1033 publications and in the country/region category (Türkiye), 83 publications are accessed. The most used words in the publications are earthquake, urban transformation, Istanbul, seismic, retrofit, assessment, structural, urban, damage, buildings and performance.2023-01-01T00:00:00ZDetermining the Suitability of Lands for Agricultural Use with the Best-Worst Method
https://hdl.handle.net/20.500.13091/5243
Title: Determining the Suitability of Lands for Agricultural Use with the Best-Worst Method
Authors: Akyüz, Gamze; Yalpır, Şükran; Ertunç, Ela
Abstract: The interaction of agricultural activities with the land starts with soil in the production part and continues until the consumption stage. Sustainable agricultural land for their use, a database regarding their current potential should be created and the land prepared based on this database should be evaluated by considering the use planning. The suitability of Ankara province lands for agricultural use by making use of the Geographical Information System analysis was carried out. To determine the suitability for agricultural use; Major Soil Groups, Land Use Capability Classes, soil depth, degree of erosion, elevation, slope, aspect, precipitation, and temperature criteria were used. This was done with the Best-Worst method, which is one of the multi-criteria decision-making approaches. It was ensured that the best and worst of the nine factors were determined by the decision makers. In the last layer, because of the analyzes made for the province of Ankara, the most appropriate land use map was created based on the natural abilities and capabilities of the lands. It has been determined that the most affecting criterion is Large Soil Groups, and Ankara's Kızılcahamam, Çankaya and Mamak districts are not very suitable for agricultural use, but other districts are generally suitable for use.2023-01-01T00:00:00ZGrafen Nanoplaka Katkılı Bazalt Elyaf Takviyeli Kompozit Boruların İç Yüzey Erozif Aşınma Direncinde Aşındırıcı Partikül Hızının Rolünün İncelenmesi
https://hdl.handle.net/20.500.13091/5246
Title: Grafen Nanoplaka Katkılı Bazalt Elyaf Takviyeli Kompozit Boruların İç Yüzey Erozif Aşınma Direncinde Aşındırıcı Partikül Hızının Rolünün İncelenmesi
Authors: Demeti, Seyit Mehmet
Abstract: Bu çalışmada [±55]4 sarım konfigürasyonunda filament sarım tekniği ile imal edilen iki farklı kompozit borunun boru içi malzeme akşının olduğu alt yapı ve malzeme aktarım uygulamalarında erozif aşınmaya maruz kalabilecek boru iç yüzeyinin erozyon davranışı dikkate alınarak araştırılmıştır. Bazalt elyaf takviyeli kompozit boru (BETKB) ile ağırlıkça %0,25 grafen nanoplakalar ile güçlendirilmiş bazalt elyaf takviyeli kompozit boruların (GNP/BETKB) katı partikül erozyon davranışları yapılan deneylerden elde edilen sonuçlar dikkate alınarak karşılaştırılmıştır. Dört farklı çarpma hızında (23 m/s, 28 m/s, 34 m/s, 53 m/s) ve üç farklı çarpma açısında (30, 45, 60) alümina aşındırıcı partiküller boru iç yüzeyine çarptırılarak elde edilen erozyon oranı değerlendirildiğinde grafen nanoplaka takviyesinin bazalt elyaf takviyeli boruda erozyon aşınmasına karşı direnci artırdığı görülmüştür. Erozyon oranının oransal değişiminin de incelendiği grafiklerde de sunulduğu üzere 28 m/s çarpma hızında %50’ye yakın bir erozyon oranı azalımı grafen nanoplaka takviyesi sayesinde elde edilmiştir. Her iki borunun aşınma modelinin yarı sünek aşınma modeline uygun bir davranış sergilediği belirlenmiştir.2023-01-01T00:00:00ZA Feasibility Analysis of the Use of ISAR Training Data in Machine Learning-Based SAR ATR
https://hdl.handle.net/20.500.13091/5245
Title: A Feasibility Analysis of the Use of ISAR Training Data in Machine Learning-Based SAR ATR
Authors: Yiğit, Enes; Demirci, Şevket; Özkaya, Umut
Abstract: Processing of synthetic aperture radar (SAR) images for automatic target recognition (ATR) is a critical application especially in military surveillance. In particular, numerous machine learning-based SAR ATR methods have been proposed for this task. However, data training and testing stages of all these methods are based on the exploitation of SAR signatures of the target under investigation. Considering the high variability of radar targets, obtaining such signature data is obviously a costly and time consuming process. In this study, therefore, a feasibility analysis of the use of inverse-SAR (ISAR) training data in SAR ATR has been made for the first time. The turntable ISAR and circular SAR images of three different vehicles are used in training and testing is performed by means of SAR images of three similar targets within the publicly available MSTAR dataset. Also, three most prominent machine learning methods, namely KNN, SVM and ANN are used in conjunction with three different feature extraction algorithms namely, GLRLM, GLSZM and GLCM. The obtained results reveal that the GLCM+SVM algorithm pair is the most effective model with 85% accuracy.2023-01-01T00:00:00Z