Elastic buckling of perforated plates subjected to linearly varying in-plane loading

Komur, M. Aydin,Sonmez, Mustafa Techno-Press 2008 Structural Engineering and Mechanics, An Int'l Jou Vol.28 No.3

Elastic buckling of perforated plates subjected to linearly varying in-plane loading

M. Aydin Komur,Mustafa Sonmez 국제구조공학회 2008 Structural Engineering and Mechanics, An Int'l Jou Vol.28 No.3

Infill wall effects on the dynamic characteristics of RC frame systems via operational modal analysis

Mehmet A. Komur,Mehmet E. Kara,Ibrahim O. Deneme 국제구조공학회 2020 Structural Engineering and Mechanics, An Int'l Jou Vol.74 No.1

This paper presents an experimental study on the dynamic characteristics of infilled reinforced concrete (RC) frames. A 1/3-scaled, one-bay, three-storey RC frame was produced and tested by using operational modal analysis (OMA). The experiments were performed on five specimens: one reference frame with no infill walls and four frames with infill walls. The RC frame systems included infill walls made of hollow clay brick, which were constructed in four different patterns. The dynamic characteristics of the patterns, including the frequency, mode shapes and damping ratios in the in-plane direction, were obtained by 6 accelerometers. Twenty-minute records under ambient vibration were collected for each model, and the dynamic characteristics were determined using the ambient vibration testing and modal identification software (ARTeMIS). The experimental studies showed that the infill walls significantly affected the frequency value, rigidity and damping ratio of the RC frame system.

Using FEM and artificial networks to predict on elastic buckling load of perforated rectangular plates under linearly varying in-plane normal load

M. Aydin Komur,Mustafa Sonmez 국제구조공학회 2010 Structural Engineering and Mechanics, An Int'l Jou Vol.34 No.2

Elastic buckling load of perforated steel plates is typically predicted using the finite element or conjugate load/displacement methods. In this paper an artificial neural network (ANN)-based formula is presented for the prediction of the elastic buckling load of rectangular plates having a circular cutout. By using this formula, the elastic buckling load of perforated plates can be calculated easily without setting up an ANN platform. In this study, the center of a circular cutout was chosen at different locations along the longitudinal x-axis of plates subjected to linearly varying loading. The results of the finite element method (FEM) produced by the commercial software package ANSYS are used to train and test the network. The accuracy of the proposed formula based on the trained ANN model is evaluated by comparing with the results of different researchers. The results show that the presented ANN-based formula is practical in predicting the elastic buckling load of perforated plates without the need of an ANN platform.

Maturation effect on strength of high-strength concretes which produced with different origin aggregates

Kaya, Mustafa,Komur, M. Aydin,Gursel, Ercin Techno-Press 2022 Advances in concrete construction Vol.14 No.2

This paper presents an application of the maturation effect on the strength of high-strength concrete which is produced with different origin aggregates. While investigating the maturation effect on HSC 384 specimens were prepared with 22 different origin aggregates. These prepared specimens were subjected to the standard compressive tests which were applied after curing for 2, 7, 28, and 56 days under appropriate conditions. The test results revealed that bright surface-low adherence behavior is valid in normal strength concretes, but is not as effective as expected in high-strength concretes. The application of artificial neural networks (ANNs) to predict 2, 7, 28, and 56 day compressive strength of HSC is also investigated in this paper. An ANN model is built, trained, and tested using the available test data gathered from experimental studies. The ANN model is found to predict 2, 7, 28, and 56 days of compressive strength of high-strength concrete well within the ranges of the input parameters considered. These comparisons show that ANNs have strong potential as a feasible tool for predicting the compressive strength of high-strength concrete within the range of the input parameters considered.

Effect of aggregate mineralogical properties on high strength concrete modulus of elasticity

Kaya, Mustafa,Komur, M. Aydin,Gursel, Ercin Techno-Press 2022 Advances in concrete construction Vol.13 No.6

Aggregates mineralogical, and petrographic properties directly affect the mechanical properties of the produced high strength. This study is focused on the effects of magmatic, sedimentary, and metamorphic aggregates on the performance of high strength concrete. In this study, the effect of the mineralogical properties of aggregates on the compressive strength and modulus of elasticity of high-strength concrete was estimated by Artifical Neural Network (ANN). To estimate the compressive strength and elasticity modules, 96 test specimens were produced. After 28 days under suitable conditions, tests were carried out to determine the compressive strength and modulus of elasticity of the test specimens. This study also focused on the application of artificial neural networks (ANN) to predict the 28-day compressive strength and the modulus of elasticity of high-strength concrete. An ANN model is developed, trained, and tested by using the available test data obtained from the experimental studies. The ANN model is found to predict the modulus of elasticity, and 28 days compressive strength of high strength concrete well, within the ranges of the input parameters. These comparisons show that ANNs have a strong potential to predict the compressive strength and modulus of elasticity of high-strength concrete over the range of input parameters considered.

Using FEM and artificial networks to predict on elastic buckling load of perforated rectangular plates under linearly varying in-plane normal load

Sonmez, Mustafa,Aydin Komur, M. Techno-Press 2010 Structural Engineering and Mechanics, An Int'l Jou Vol.34 No.2

Elastic buckling load of perforated steel plates is typically predicted using the finite element or conjugate load/displacement methods. In this paper an artificial neural network (ANN)-based formula is presented for the prediction of the elastic buckling load of rectangular plates having a circular cutout. By using this formula, the elastic buckling load of perforated plates can be calculated easily without setting up an ANN platform. In this study, the center of a circular cutout was chosen at different locations along the longitudinal x-axis of plates subjected to linearly varying loading. The results of the finite element method (FEM) produced by the commercial software package ANSYS are used to train and test the network. The accuracy of the proposed formula based on the trained ANN model is evaluated by comparing with the results of different researchers. The results show that the presented ANN-based formula is practical in predicting the elastic buckling load of perforated plates without the need of an ANN platform.

Concurrency of Guillain-Barre syndrome and acute transverse myelitis: a case report and review of literature

Tolunay, Orkun,Celik, Tamer,Celik, Umit,Komur, Mustafa,Tanyeli, Zeynep,Sonmezler, Abdurrahman The Korean Pediatric Society 2016 Clinical and Experimental Pediatrics (CEP) Vol.59 No.no.sup1

Guillain-$Barr{\acute{e}}$ syndrome and acute transverse myelitis manifest as demyelinating diseases of the peripheral and central nervous system. Concurrency of these two disorders is rarely documented in literature. A 4-year-old girl presenting with cough, fever, and an impaired walking ability was admitted to hospital. She had no previous complaints in her medical history. A physical examination revealed lack of muscle strength of the lower extremities and deep tendon reflexes. MRI could not be carried out due to technical problems; therefore, both Guillain-$Barr{\acute{e}}$ syndrome and acute transverse myelitis were considered for the diagnosis. Intravenous immunoglobulin treatment was started as first line therapy. Because this treatment did not relieve the patient's symptoms, spinal MRI was carried out on the fourth day of admission and demyelinating areas were identified. Based on the new findings, the patient was diagnosed with acute transverse myelitis, and high dose intravenous methylprednisolone therapy was started. Electromyography findings were consistent with acute polyneuropathy affecting both motor and sensory fibers. Therefore, the patient was diagnosed with concurrency of Guillain-$Barr{\acute{e}}$ syndrome and acute transverse myelitis. Interestingly, while concurrency of these 2 disorders is rare, this association has been demonstrated in various recent publications. Progress in diagnostic tests (magnetic resonance imaging and electrophysiological examination studies) has enabled clinicians to establish the right diagnosis. The possibility of concurrent Guillain-$Barr{\acute{e}}$ syndrome and acute transverse myelitis should be considered if recovery takes longer than anticipated.