Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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Subject: search.filters.subject.Empirical model

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  • Article
    Citation - WoS: 8
    Citation - Scopus: 9
    Empirical, Numerical, and Soft Modelling Approaches for Non-Cohesive Sediment Transport
    (Springer, 2021) Tayfur, Gökmen
    This paper reviews the modelling approaches and outstanding issues with regard to non-cohesive sediment transport which has been experimentally and numerically studied for many decades owing to its importance to hydraulic structures, morphology and related areas. About 311 papers are reviewed that included laboratory experiments, field observations, and analytical and numerical modelling studies. The reviewed papers cover the period 1938-2020. Of 311, 95 papers are included in this paper. The modeling approaches include empirical, physics-based, spatially averaged, and soft methods. The empirical models have oversimplified the process while the physics-based models are indispensable when the detailed analysis is required. On the other hand, when the objective is to obtain cumulative sediment loads, it would be advantageous to employ the spatial averaging modelling and/or the soft computing methods due to less computational burden and data requirements. The outstanding issues are related to the particle fall velocity, particle velocity, incipient motion, and transport function that require further experimental investigations especially for unsteady non-uniform transport processes.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 6
    Prediction 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, Ebru
    Based 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: 1
    Citation - Scopus: 1
    Generalized 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, Gökmen
    Gypsum 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: 18
    Citation - Scopus: 24
    Empirical Sediment Transport Models Based on Indoor Rainfall Simulator and Erosion Flume Experimental Data
    (John Wiley and Sons Inc., 2017) Aksoy, Hafzullah; Eriş, Ebru; Tayfur, Gökmen
    Land degradation processes start with accelerated runoff and sediment delivery. In this study, rainfall-runoff induced sediment transport is investigated using data from an indoor laboratory experimental setup consisting of a rainfall simulator and an erosion flume. The data are analysed to develop empirical models using sediment discharge, slope, flow discharge, rainfall intensity and sediment size. Fine and medium sands are considered as bare soil in experiments. Four rainfall intensities (45, 65, 85 and 105 mm h−1) are applied with combinations of lateral and longitudinal slopes of 5%, 10%, 15% and 20%. Eighty experiments are conducted. Flow is measured, and sediment within flow is separated and weighted. Experimental data are used for developing empirical models through multiple regression with parameters optimized by genetic algorithm. Results show that slope is the main contributing variable to the sediment transport over hillslopes. Accommodating variables among slope, rainfall intensity, flow discharge and median diameter of sediment as independent variables, one-variable, two-variable and four-variable models are developed considering also that higher number of parameters increases the performance of the model with higher cost of parameterization.