Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 5Citation - Scopus: 6Prediction of Rainfall Runoff-Induced Sediment Load From Bare Land Surfaces by Generalized Regression Neural Network and Empirical Model(Wiley, 2020) Tayfur, Gökmen; Aksoy, Hafzullah; Eriş, EbruBased on three rainfall run-off-induced sediment transport data for bare surface experimental plots, the generalized regression neural network (GRNN) and empirical models were developed to predict sediment load. Rainfall intensity, slope, rainfall duration, soil particle median diameter, clay content of the soil, rill density and soil particle mass density constituted the input variables of the models while sediment load was the target output. The GRNN model was trained and tested. The GRNN model was found successful in predicting sediment load. Sensitivity analysis by the GRNN model revealed that slope and rainfall duration were the most sensitive parameters. In addition to the GRNN model, two empirical models were proposed: (1) in the first empirical model, all the input variables were related to the sediment load, and (2) in the second empirical model, only rainfall intensity, slope and rainfall duration were related to the sediment load. The empirical models were calibrated and validated. At the calibration stage, the coefficients and the exponents of the empirical models were obtained using the genetic algorithm optimization method. The validated empirical models were also applied to two more experimental data sets: (1) one data set was from a field experiment, and (2) one set was from a laboratory experiment. The results indicated the success of the empirical models in predicting sediment load from bare land surfaces.Article Citation - WoS: 1Citation - Scopus: 1Generalized Regression Neural Network and Empirical Models To Predict the Strength of Gypsum Pastes Containing Fly Ash and Blast Furnace Slag(Springer Verlag, 2020) Erdem, Tahir Kemal; Cengiz, Okan; Tayfur, GökmenGypsum is widely used in constructions owing to its easy application, zero shrinkage, and excellent fire resistance. Several parameters can affect the properties of gypsum pastes. To study the strength of the gypsum pastes experimentally by trying all these parameters is time-consuming and costly. Therefore, artificial intelligence methods can be very useful to predict the paste strength, which, in turn, can reduce the number of trial batches. Based on experimental data, the generalized regression neural network (GRNN) and empirical models were developed to predict strength of gypsum pastes containing fly ash (FA) and blast furnace slag (BFS). Gypsum content, pozzolan content, curing temperature, curing duration, and testing age constituted the input variables of the models while the paste strength was the target output. The trained and tested GRNN model was found to be successful in predicting strength. Sensitivity analysis by the GRNN model revealed that the curing duration and temperature were important sensitive parameters. In addition to the GRNN model, empirical models were proposed for the strength prediction. The same input variables formed the input vectors of the empirical models. The same dataset used for the calibration of the GRNN model was employed to establish the empirical models by employing genetic algorithm (GA) method. The empirical models were successfully validated. The GRNN and GA_based empirical models were also tested against the multi-linear regression (MLR) and multi-nonlinear regression (MNLR) models. The results showed the outperformance of the GRNN and the GA_based empirical models over the others.Article Citation - WoS: 24Citation - Scopus: 24GA-optimized model predicts dispersion coefficient in natural channels(IWA Publishing, 2009) Tayfur, GökmenModels whose parameters were optimized by genetic algorithm (GA) were developed to predict the longitudinal dispersion coefficient in natural channels. Following the existing equations in the literature, ten different linear and nonlinear models were first constructed. The models relate the dispersion coefficient to flow and channel characteristics. The GA model was then employed to find the optimal values of the constructed model parameters by minimizing the mean absolute error function (objective function). The GA model utilized an 80% cross-over rate and 4% mutation rate. It started each computation with a population of 100 chromosomes in the gene pool. For each model, while minimizing the objective function, the values of the model parameters were constrained between [-10, +10] at each iteration. The optimal values of the model parameters were obtained using a calibration set of 54 out of 80 sets of measured data. The minimum error was obtained for the case where the model was a linear equation relating dispersion coefficient to flow discharge. The model performance was then satisfactorily tested against the remaining 26 measured validation datasets. It performed better than the existing equations. it yielded minimum errors of MAE = 21.4m2/s (mean absolute error) and RMSE = 28.5m2/s (root mean-squares error) and a maximum accuracy rate of 81%. © IWA Publishing 2009.