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- MakaleYalnız künye2025Scopus SJRQ2Using Meta-Heuristic Optimization Algorithms to Determine Baseflow and Comparing Their Temporal Performance
Acar, Ramazan; Sapliogu, Kemal
Accurate and simple determination of baseflow is an extremely important issue. In this study, the baseflow levels of Göksu station number 1805 in the Seyhan basin were determined by calibrating the Lyne and Hollick, Chapman, Chapman and Maxwell methods available in the literature using meta-heuristic optimization methods. The meta-heuristic algorithms used for calibration in the study were run thirty times each. Thus the reliability, the reliability of the algorithms was tested with the standard deviations obtained. The study also measured the temporal performance of the algorithms. In addition, the baseflows of Göksu station were determined and their percentage rates were found separately according to all three methods and intuitive methods, and the results obtained were compared. By examining the results obtained in this section, the average baseflow rate of the basin was also determined. Furthermore, each phase of the study was repeated over a five-year period, with the objective of measuring its sustainability. Consequently, it has been demonstrated that the method can be employed over extended periods.
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İncele - MakaleYalnız künye2023Scopus SJRQ2An Estimation Proposal for Engineering Properties of Modified Concrete by using Standalone and Hybrid GRELM
Cemalgil, Selim; Onat, Onur; Aruntaş, Hüseyin Yılmaz
The presented study pertains to an attempt to propose a novel prediction model to predict the flexural and compressive strengths of concrete modulated using steel fiber (SFb) and silica fume (SF). A completed experimental investigation is adopted for the current study, and a research plan is employed. Three different superplasticizers amount (SP), different SF replacement ratios, and a constant amount of SFb were used by the weight of cement to meet the C25 target strength. A sum of 16 distinct mixtures designed by changing SP and SF ratios were developed. Furthermore, SFb was added at a fixed rate of 65 kg/m3 to all planned concrete mixes. In addition, SFb was used to create a 16-mix design. Finally, a total of 32 distinct mix designs were created. Produced, hardened specimens were exposed to two different curing conditions. This research uses the mechanical characteristics of concrete treated with SF, SP, and SFb to estimate by conducting standalone and hybridized generalized extreme learning machine (GRELM) algorithms based on available experimental data in terms of the metaheuristic aspects of this work. With continuous input data, four separate data sets were constructed. Compressive strength and flexural strength were estimated separately. With the aid of the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms, binary and ternary hybrid approaches were developed and tested on the data. Four distinct estimation models were suggested. Two quality metrics were used to evaluate the estimation performance: Root Mean Square Error (RMSE) and correlation of determination (R2). The estimation results showed that the hybridized GRELM-PSO-GWO estimation model that was built for prediction was relatively successful in all sets.
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İncele - MakaleYalnız künye2022Scopus SJRQ1A novel prediction model for durability properties of concrete modified with steel fiber and Silica Fume by using Hybridized GRELM
Cemalgil, Selim; Gül, Enes; Onat, Onur; Yılmaz Aruntaş, Hüseyin
The service life performance of conventional and modified concrete subjected to harsh climatic condition environment is directly related to durability properties of concrete like abrasion, freezing and thawing cycles. These properties are critical issues that should be predicted before performing experimental test. On this basis, the basic purpose of this paper is to predict the abrasion loss, freezing and thawing properties of concrete modified with silica fume (SF) and steel fiber (SFb) by using mix design and additional properties. From this point of view, a conducted experimental study was selected as a case study. In the control concrete (CC) mixtures, Portland cement, crushed stone aggregate, and superplasticizer (SP) were used in the selected experimental study. SP in concrete mixtures was used in the amounts of 1.0%, 1.5%, and 2.0% by weight of cement, and so modified concrete was produced with and without SFb according to the target strength of C25. Furthermore, SF and SFb were used in different amounts to modify the concrete. The SF was replaced with cement in the amounts of 7.5%, 10.0%, and 15.0%. In total, 16 different mix designs were prepared with different SP and SF ratios. In addition, SFb was added to all mixtures of designed concrete at a constant amount of 65 kg/m3. Additionally, a 16-mix design was prepared with SFb. Cumulatively, 32 different mix designs were prepared for the experimental study. Tests on the fresh, hardened, and life-cycle performance properties of the concrete were conducted. As for the metaheuristic part of this study, on the basis of the available experimental data, life-cycle performance parameters of the concrete modified with SF and SFb are predicted by using single and hybrid generalized extreme learning machine methods. Eight different data sets were generated with gradually extended input data. Two different outputs were considered: abrasion resistance (AL) and freezing/thawing (FT). Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms were used to produce binary and ternary hybrid methods. Four different models were proposed as listed: single use of Generalized Extreme Learning Machine (GRELM), binary use of GRELM-PSO, and GRELM-GWO. Finally, PSO and GWO were hybridized and integrated into GRELM. Two quality indicators, namely Root Mean Square Error (RMSE) and correlation of determination (R2), were considered to see the performance of the prediction. The results showed that the proposed ternary prediction model composed of GRELM-PSO-GWO provided more accurate results in all sets from 74% to 91% by extending input parameters, even if complicated parameters are inserted in as an input to the data set.
AtıfScopus28WoS22OpenAlex25İncele - MakaleYalnız künye2020Scopus SJRQ2Optimization of open channels using particle swarm optimization algorithm
Saplioglu, Kemal; Ozturk, Tulay Sugra Kucukerdem; Acar, Ramazan
Due to recent increasing water demand, planning and projecting of water resources has resulted in increased costs. There-fore, it is important to obtain optimum results in project planning. In this study, dimensioning of open channels used espe-cially for irrigation purposes has been studied using a particle swarm optimization algorithm to investigate optimum base width, channel height, and slope angles. The results are summarized in graphs and tables. In the study, it was found that the optimum slope angle varied between 0. 200. 45. Furthermore, it was found that increasing the slope angle significant-ly increased costs. Finally, the increase in flow increases costs but the rate of increase diminishes.
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