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Modelling the reinforced concrete beams strengthened with GFRP against shear crack
Mustafa Kaya,Canberk Yaman 사단법인 한국계산역학회 2018 Computers and Concrete, An International Journal Vol.21 No.2
In this study, the behavior of the number of anchorage bolts on the glass-fiber reinforced polymer (GFRP) plates adhered to the surfaces of reinforcing concrete (RC) T-beams was investigated analytically. The analytical results were compared to the test results in term of shear strength, and midpoint displacement of the beam. The modelling of the beams was conducted in ABAQUS/CAE finite element software. The Concrete Damaged Plasticity (CDP) model was used for concrete material modeling, and Classical Metal Plasticity (CMP) model was used for reinforcement material modelling. Model-1 was the reference specimen with enough sufficient shear reinforcement, and Model-2 was the reference specimen having low shear reinforcement. Model-3, Model-4 and Model-5 were the specimens with lower shear reinforcement. These models consist of a single variable which was the number of anchorage bolts implemented to the GFRP plates. The anchorage bolts of 2, 3, and 4 were mutually mounted on each GFRP plates through the beam surfaces for Model-3, Model-4, and Model-5, respectively. It was found that Model-1, Model-3, Model-4 and Model-5 provided results approximately equal to the test results. The results show that the shear strength of the beams increased with increasing of anchorage numbers. While close results were obtained for Model-1, Model-3, Model-4 and Model-5, in Model-2, the rate of increase of displacement was higher than the increase of load rate. It was seen, finite element based ABAQUS program is inadequate in the modeling of the reinforced concrete specimens under shear force.
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Murat Yassa,Memis Ali Mutlu,Pinar Biro,Taha Yusuf Kuzan,Erkan Kalafat,Canberk Usta,Emre Yavuz,Ilkhan Keskin,Niyazi Tug 대한초음파의학회 2020 ULTRASONOGRAPHY Vol.39 No.4
Purpose: This study investigated interobserver agreement in lung ultrasonography (LUS) in pregnant women performed by obstetricians with different levels of expertise, with confirmation by an expert radiologist. Methods: This prospective study was conducted at a tertiary "Coronavirus Pandemic Hospital" in April 2020. Pregnant women suspected to have coronavirus disease 2019 (COVID-19) were included. Two blinded experienced obstetricians performed LUS on pregnant women separately and noted their scores for 14 lung zones. Following a theoretical and hands-on practical course, one experienced obstetrician, two novice obstetric residents, and an experienced radiologist blindly evaluated anonymized and randomized still images and videoclips retrospectively. Weighted Cohen's kappa and Krippendorff’s alpha tests were used to assess the interobserver agreement. Results: Fifty-two pregnant women were included, with confirmed COVID-19 diagnosis rate of 82.7%. In total, 336 eligible still images and 115 videoclips were included in the final analysis. The overall weighted Cohen’s kappa values ranged from 0.706 to 0.912 for the 14 lung zones. There were only seven instances of major disagreement (>1 point) in the evaluation of 14 lung zones of 52 patients (n=728). The overall agreement between the radiologist and obstetricians for the still images (Krippendorff's α=0.856, 95% confidence interval [CI], 0.797 to 0.915) and videoclips (Krippendorff's α=0.785; 95% CI, 0.709 to 0.861) was good. Conclusion: The interobserver agreement between obstetricians with different levels of experience on still images and videoclips of LUS was good. Following a brief theoretical course, obstetricians' performance of LUS in pregnant women and interpretation of pre-acquired LUS images can be considered consistent.
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Energy-Efficient RL-Based Aerial Network Deployment Testbed for Disaster Areas
Ariman, Mehmet,Akkoc, Mertkan,Talip Sari, Tolga,Erol, Muhammed Rasit,Seçinti, Gökhan,Canberk, Berk 한국통신학회 2023 Journal of communications and networks Vol.25 No.1
Rapid deployment of wireless devices with 5G andbeyond enabled a connected world. However, an immediatedemand increase right after a disaster paralyzes network in-frastructure temporarily. The continuous flow of information iscrucial during disaster times to coordinate rescue operations andidentify the survivors. Communication infrastructures built for users of disaster areasshould satisfy rapid deployment, increased coverage, and avail-ability. Unmanned air vehicles (UAV) provide a potential solutionfor rapid deployment as they are not affected by traffic jamsand physical road damage during a disaster. In addition, ad-hocWiFi communication allows the generation of broadcast domainswithin a clear channel which eases one-to-many communications. Moreover, using reinforcement learning (RL) helps reduce thecomputational cost and increases the accuracy of the NP-hardproblem of aerial network deployment. To this end, a novel flying WiFi ad-hoc network managementmodel is proposed in this paper. The model utilizes deep-Q-learning to maintain quality-of-service (QoS), increase userequipment (UE) coverage, and optimize power efficiency. Fur-thermore, a testbed is deployed on Istanbul Technical Univer-sity (ITU) campus to train the developed model. Training resultsof the model using testbed accumulates over 90% packet deliveryratio as QoS, over 97% coverage for the users in flow tables, and0.28 KJ/Bit average power consumption.
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