Halid Akdemir’s scientific contributions

The amount of daily water demanded by individuals is used as a basic parameter in the design of infrastructure systems. The purpose of this study is to examine the daily average water consumption per capita (WCPC) values used as infrastructure basic design parameters and suggested by Turkish Standards across Turkey. Accordingly, one of the aims of this study is to reveal how accurately these standards predict WCPC values, the other one is to create a model predicting better with low error rate and trend-reflecting values. WCPC belonging to 2018 for 30 Turkish cities was introduced. According to this research study, the population weighted average WCPC was found out to be 131.9 L across the country and it ranges from 67.7 to 208 L. The population weighted average loss percent of discharge across the country is 36% while it ranges from 23% to 71%. 10 parameters for each city that had the potential to influence water consumption are following; average temperature, maximum temperature, average precipitation, humidity, water price, population, population density, sunshine duration, tourism intensity, and industry level. Since the problem is complex, the fuzzy logic method, which is a rule-based algorithm from the soft computing methods, was preferred and found suitable on the stage of creating the predictor model. The fuzzy model was formed with an expert perspective approach. The accuracy of the values proposed by the fuzzy model and the standards were measured and compared with R2 and RMSE parameters. The coefficient of determination of Altınbilek’s values, 2013 Provincial Bank, which are Turkish Standards, and the fuzzy model were found out as −4.77, −0.55, and 0.41, respectively. The poor estimation ability of the standards has revealed the need for the model that is able to make better estimation and measurement results proved the necessity of future examination of the predictions.KeywordsDaily average water consumption per capitaFuzzy logicWater consumption of Turkey

Wakes and vortices are commonly observed in fluid flows around bluff bodies, a phenomenon which is called vortex shedding. Such vortices are named as von Kármán vortices since their first investigation is performed by the leading fluid dynamicist Theodore von Kármán. Although initially observed in the studies of fluid flows, the same phenomenon can also be observed in different branches of mediums such as condensates. It is possible to model these vortices using numerical techniques that solve the Navier-Stokes equations, however, some dynamic equations such as the complex Ginzburg-Landau (GL) equation is another frequently used model for these purposes. In this paper, we solve the GL equation using a spectral scheme and Runge-Kutta time integrator to simulate the dynamics of von Kármán vortices around a cylinder. The prediction of temporal dynamics is of crucial importance to avoid excessive shedding, resonance, and structural damage of the engineering structures. With this motivation, here we examine the predictability of the von Kármán vortices using the adaptive neuro-fuzzy inference system (ANFIS) which relies on a rule-based relationship between input values and output values that are learned adaptively by being trained with the data set analyzed. We show that the temporal dynamics of the von Kármán vortices can be adequately performed by ANFIS and we report the prediction success of the ANFIS in the solution of this complex prediction problem measured by the coefficient of determination $({R}^{2})$ and the root mean square error (RMSE) values. Our results can be used for predicting, interpolating, and extrapolating vortex data to analyze fluid dynamics problems and to develop control strategies for avoiding structural failures.Keywordsvon Kármán vorticesGinzburg-Landau equationANFIS

Although dam failures are not very common in the course of engineering history, they can be catastrophic disasters causing many life losses, so they need to be investigated. Dam failures are difficult problems to analyze due to the complexity of the associated parameters which can be very hard to determine. With this motivation, in this study, grey relational analysis (GRA) was used to rank the danger levels of Turkey’s aging 15 dams in case of a possible collapse. The failure mode of each dam was assumed to be a sudden collapse, thus breach development is not considered. This type of failure mode is more commonly observed during major earthquakes. The dams chosen for this study have been selected from the engineering point of view as they have the highest hazard potential in case of failure across the country. Accordingly, the important involved attributes of the model were determined as follows: surrounding population, distance from that population, elevation relative to that population, and reservoir size. The weights of these involved attributes were preferred as 0.40, 0.30, 0.10, and 0.20, respectively. In conjunction with the literature on the subject, the distinguished coefficient was selected as 0.5. The risk assessment based on the GRA results is performed for Turkey’s 15 dams involved in the study. The output of this study will contribute to the disaster and risk management policies of Turkey’s dams and will have similar applications worldwide.KeywordsGrey relational analysisDam failuresDanger level rankingTurkey’s dams

Although dam failures are not very common in the course of engineering history, they can be catastrophic disasters causing many life losses, so they need to be investigated. Dam failures are difficult problems to analyze due to the complexity of the associated parameters which can be very hard to determine. With this motivation, in this study, grey relational analysis (GRA) was used to rank the danger levels of Turkey's aging 15 dams in case of a possible collapse. The failure mode of each dam was assumed to be a sudden collapse, thus breach development is not considered. This type of failure mode is more commonly observed during major earthquakes. The dams chosen for this study have been selected from the engineering point of view as they have the highest hazard potential in case of failure across the country. Accordingly, the important involved attributes of the model were determined as follows: surrounding population, distance from that population, elevation relative to that population , and reservoir size. The weights of these involved attributes were preferred as 0.40, 0.30, 0.10, and 0.20, respectively. In conjunction with the literature on the subject, the distinguished coefficient was selected as 0.5. The risk assessment based on the GRA results is performed for Turkey's 15 dams involved in the study. The output of this study will contribute to the disaster and risk management policies of Turkey's dams and will have similar applications worldwide .

The amount of daily water demanded by individuals is used as a basic parameter in the design of infrastructure systems. The purpose of this study is to examine the daily average water consumption per capita (WCPC) values used as infrastructure basic design parameters and suggested by Turkish Standards across Turkey. Accordingly, one of the aims of this study is to reveal how accurately these standards predict WCPC values, the other one is to create a model predicting better with low error rate and trend-reflecting values. WCPC belonging to 2018 for 30 Turkish cities was introduced. According to this research study, the population weighted average WCPC was found out to be 131.9 liters across the country and it ranges from 67.7 to 208 liters. The population weighted average loss percent of discharge across the country is 36% while it ranges from 23% to 71%. 10 parameters for each city that had the potential to influence water consumption are following; average temperature, maximum temperature, average precipitation, humidity, water price, population, population density, sunshine duration, tourism intensity, and industry level. Since the problem is complex, the fuzzy logic method, which is a rule-based algorithm from the soft computing methods, was preferred and found suitable on the stage of creating the predictor model. The fuzzy model was formed with an expert perspective approach. The accuracy of the values proposed by the fuzzy model and the standards were measured and compared with R 2 and RMSE parameters. The coefficient of determination of Altınbilek's values, 2013 Provincial Bank, which are Turkish Standards, and the fuzzy model were found out as-4.77,-0.55, and 0.41, respectively. The poor estimation ability of the standards has revealed the need for the model that is able to make better estimation and measurement results proved the necessity of future examination of the predictions.

Wakes and vortices are commonly observed in fluid flows around bluff bodies, a phenomenon which is called vortex shedding. Such vortices are named as von Kármán vortices since their first investigation is performed by the leading fluid dynamicist Theodore von Kármán. Although initially observed in the studies of fluid flows, the same phenomenon can also be observed in different branches of mediums such as condensates. It is possible to model these vor-tices using numerical techniques that solve the Navier-Stokes equations, however , some dynamic equations such as the complex Ginzburg-Landau (GL) equation is another frequently used model for these purposes. In this paper, we solve the GL equation using a spectral scheme and Runge-Kutta time integrator to simulate the dynamics of von Kármán vortices around a cylinder. The prediction of temporal dynamics is of crucial importance to avoid excessive shedding, resonance, and structural damage of the engineering structures. With this motivation , here we examine the predictability of the von Kármán vortices using the adaptive neuro-fuzzy inference system (ANFIS) which relies on a rule-based relationship between input values and output values that are learned adaptively by being trained with the data set analyzed. We show that the temporal dynamics of the von Kármán vortices can be adequately performed by ANFIS and we report the prediction success of the ANFIS in the solution of this complex prediction problem measured by the coefficient of determination () and the root mean square error () values. Our results can be used for predicting , interpolating, and extrapolating vortex data to analyze fluid dynamics problems and to develop control strategies for avoiding structural failures.