Estimation of saturated hydraulic conductivity from double‐ring infiltrometer measurements

Abstract

This research aims to determine soil vertical saturated hydraulic conductivity (K s ) in situ from the measured steady infiltration rate (I), initial soil properties and double-ring infiltrometer (DRI) test data. Characterizing the effects of these variables on the measured steady infiltration rate will enable more accurate prediction of K s . We measured the effects of the ring diameter, head of ponding, ring depth, initial effective saturation and soil macroscopic capillary length on measured steady infiltration rates. We did this by simulating 864 DRI tests with the finite element program HYDRUS-2D and by conducting 39 full-scale in situ DRI tests, 30 Mini-Disk infiltrometer experiments and four Guelph Permeameter tests. The M5 ′ model trees and genetic programming (GP) methods were applied to the data to establish formulae to predict the K s of sandy to sandy-clay soils. The nine field DRI tests were used to verify the computer models. We determined the accuracy of the methods with 30% of the simulated DRI data to compare I/K S values of the finite element models with estimates from the suggested formulae. We also used the suggested formulae to predict the K s values of 30 field DRI experiments and compared them with values measured by Guelph Permeameter tests. Compared with the GP method, the M5 ′ model was better at predicting K S , with a correlation coefficient of 0.862 and root mean square error (RMSE) of 0.282 cm s −1 . In addition, the latter method estimated K s values of the field experiments more accurately, with an RMSE of 0.00346 cm s −1 . Highlights• Effects of initial soil properties and DRI test data on steady infiltration rate are studied. • M5 ′ model algorithm and genetic programming method are applied on the data to formulate the equations.• Compared to the GP method, the M5 ′ model was better at predicting K s . • The equation from M5 ′ tree model is robust for estimating in situ K s with the DRI.

A review of Genetic Programming and Artificial Neural Network applications in pile foundations

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“…In addition, there is also a bias with modifiable weighted connections, which is only connected to neurons in the hidden and output layers. ANNs can be autonomous and learn by input from outside "teachers" or even self-teaching from written in rules [6]. Typical structure and operation of ANNs is shown in Fig.…”

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“…2 [6] The parameters that were studied for comparison of different applications include: type of ANN model, characteristics of dataset, soil type, input and output variables, error criteria used for validation with respective error value, and selected method of comparison.…”

The effects of initial soil moisture conditions on swale flow hydrographs

“…The dissipation of swale flow volumes is strongly affected by infiltration into soils, which is commonly described by the saturated hydraulic conductivity, K s , and estimated from the literature data for soils of various textures (Lee, Traver, & Welker, 2016), field measurements with infiltrometers (Ahmed et al, 2015;Fatehnia, Tawfiq, & Ye, 2016;Nevada Tahoe Conservation District, 2014), or from water balance considerations (Wanielista & Yousef, 1992). Applications of these methods are exposed to high uncertainties resulting from great variability of infiltration rates (Ahmed et al, 2015).…”