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Showing 2 results for Soft Computing
Parametric Estimation of the Van Genuchten and Fredlund and Xing’s Moisture Retention Curve Models Using Soft Computing Techniques in Some Khozestan Soils
F. Moradi, B. Khalilimoghadam, S. Jafari, S. Ghorbani Dashtaki,
Volume 18, Issue 69 (12-2014)
Abstract

Soft computing techniques have been extensively studied and applied in the last three decades for scientific research and engineering computing. The purpose of this study was to investigate the abilities of multilayer perceptron neural network (MLP) and neuro-fuzzy (NF) techniques to estimate the soil-water retention curve (SWRC) from Khozestan sugarcane Agro-Industries data. Sensitivity analysis was used for determining the model inputs and appropriate data subset. Also, in this paper, the van Genuchten and Fredlund and xing models were used to predict SWRC. Measured soil variables included particle size distribution, organic matter, bulk density, calcium carbonate, sodium adsorption ratio, electrical conductivity, acidity, mean weight diameter, plastic and liquid limit, resistance of soil penetration, water saturation percentage and water content for matric potentials -33, -100, -500 and -1500 kPa. The results of this study in terms of various statistical indices indicated that both MLP and NF provide good predictions but the neural network provides better predictions than neuro-fuzzy model. For example, using MLP and NF models values of NMSE at prediction θs, θr, α, n and m in Fredlund and Xing equation corresponded to (0.059, 0.065), (0.154, 0.162), (0.109, 0.117), (0.129, 0.135) and (0.129, 0.145), respectively. Furthermore, α and n parameters at the first depth, and θr and α parameters at the second depth in Fredlund and Xing equation were estimated with higher accuracy compared with equivalent parameters in van Genuchten equation


Application of SVM Algorithm in Predicting Vertical Pier Scour Depth
M. Majedi Asl, S. Valizadeh,
Volume 23, Issue 4 (12-2019)
Abstract
Local scour around the foundation of marine and hydraulic structures is one of the most important factors in the instability and destruction of these structures. False prediction of scour depth around bridges has caused financial losses in plasticization and endangered many people's lives. Therefore, an accurate estimation of this complex phenomenon around the bridges is necessary. Also, since the formulas presented by different researchers relate to laboratory conditions, they are less true and less accurate in other situations. Recently, many researchers have tried to introduce new methods and models called soft calculations in predicting this phenomenon. In this research, 146 different laboratory data series (three different laboratory conditions) were analyzed using a backup vector machine to predict scour depth around the bridge head. These data are presented in the form of various combinations of input parameters  which, respectively, represent thickness under the slippery layer, Reynolds number, critical velocity, Shields parameter, velocity Shear, average speed, flow depth, the average diameter of the particles and diameter of the bridge. The parameters in two different scenarios (the mode with dimension and mode) were introduced into the SVM network and the results of this machine were compared with those obtained from the experimental formulas and relations presented in this study. The results showed that in the first scenario, the combination of No. 5 with input parameters () and in the second scenario, the combination No. 5 with input parameters  () for the test stage were selected as the best model. It was also concluded from the results that the scenario two (the state with dimension) in predicting the scour depth around the vertical single-pillar provided a more accurate estimate than the first scenario (barrier state). At the end, the sensitivity analysis was carried out on the parameters and the parameters D, U*, V were selected, respectively, as the most effective parameters

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