Browsing by Author "Ipek, Cengiz"

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Performance of a Seismically Isolated Building in the Earthquakes of February 6, 2023, in Türkiye
(Asce-amer Soc Civil Engineers, 2025) Kim, Hyun-Myung; Ipek, Cengiz; Tapan, Mucip; Constantinou, Michael C.
On February 6, 2023, a devastating earthquake of M7.8 struck T & uuml;rkiye with an epicenter to the east of the city of Gaziantep. Within hours, it was followed by a M7.5 earthquake some 107 km to the north. The focus of this paper is the performance of a seismically isolated hospital in Adana at distance of 158 km to the east of the epicenter of the M7.8 earthquake. Despite the large distance from the epicenter, the ground shaking was large enough to activate the triple friction pendulum isolators of the building, resulting in a beneficial reduction of accelerations directly above the isolators and avoidance of any structural or nonstructural damage. After baseline correction and filtering of the raw data of the instruments at the hospital, we obtained, by integration of acceleration records, estimates the isolator displacement histories. We performed analysis of the building using properties of the isolators obtained from the production tests on all 1,512 isolators and then adjusting the properties for their 8-year age. Based on comparison of the analysis results and processed records of the building's acceleration and displacement histories, we obtained estimates of isolator properties and confirmed that the isolators primarily responded with motion of the first pendulum (or in Regime I), which is characterized by high stiffness and low friction, leading to some reduction of response in weak seismic ground motion.
Scanm: a Novel Hybrid Metaheuristic Algorithm and Its Comparative Performance Assessment
(Aves, 2022) Kayri, Murat; Ipek, Cengiz; Izci, Davut; Eker, Erdal
This paper proposes a novel sine-cosine and Nelder-Mead (SCANM) algorithm which hybridizes the sine-cosine algorithm (SCA) and Nelder-Mead (NM) local search method. The original version of SCA is prone to early convergence at the local minimum. The purpose of the SCANM algorithm is to overcome this issue. Thus, it aims to overcome this issue with the employment of the NM method. The SCANM algorithm was firstly compared with the SCA algorithm through 23 well-known test functions. The statistical assessment confirmed the better performance of the proposed algorithm. The comparative convergence profiles further demonstrated the significant performance improvement of the proposed SCANM algorithm. Besides, a non-parametric test was performed, and the results that showed the ability of the proposed approach were not by coincidence. A popular and well-performed metaheuristic algorithm known as grey wolf optimization was also used along with the recent and promising two other algorithms (Archimedes optimization and Harris hawks optimization) to comparatively demonstrate the performance of the SCANM algorithm against well-known classical benchmark functions and CEC 2017 test suite. The comparative assessment showed that the SCANM algorithm has promising performance for optimization problems. The non-parametric test further verified the better capability of the proposed SCANM algorithm for optimization problems.