Abstract: The California Bearing Ratio (CBR) has been
acknowledged as an important parameter to characterize the bearing
capacity of earth structures, such as earth dams, road embankments,
airport runways, bridge abutments and pavements. Technically, the
CBR test can be carried out in the laboratory or in the field. The CBR
test is time-consuming and is infrequently performed due to the
equipment needed and the fact that the field moisture content keeps
changing over time. Over the years, many correlations have been
developed for the prediction of CBR by various researchers,
including the dynamic cone penetrometer, undrained shear strength
and Clegg impact hammer. This paper reports and discusses some of
the results from a study on the prediction of CBR. In the current
study, the CBR test was performed in the laboratory on some finegrained
subgrade soils collected from various locations in Victoria.
Based on the test results, a satisfactory empirical correlation was
found between the CBR and the physical properties of the
experimental soils.
Abstract: Saturated hydraulic conductivity is one of the soil
hydraulic properties which is widely used in environmental studies
especially subsurface ground water. Since, its direct measurement is
time consuming and therefore costly, indirect methods such as
pedotransfer functions have been developed based on multiple linear
regression equations and neural networks model in order to estimate
saturated hydraulic conductivity from readily available soil
properties e.g. sand, silt, and clay contents, bulk density, and organic
matter. The objective of this study was to develop neural networks
(NNs) model to estimate saturated hydraulic conductivity from
available parameters such as sand and clay contents, bulk density,
van Genuchten retention model parameters (i.e. r
θ , α , and n) as well
as effective porosity. We used two methods to calculate effective
porosity: : (1) eff s FC φ =θ -θ , and (2) inf φ =θ -θ eff s , in which s
θ is
saturated water content, FC θ is water content retained at -33 kPa
matric potential, and inf θ is water content at the inflection point.
Total of 311 soil samples from the UNSODA database was divided
into three groups as 187 for the training, 62 for the validation (to
avoid over training), and 62 for the test of NNs model. A commercial
neural network toolbox of MATLAB software with a multi-layer
perceptron model and back propagation algorithm were used for the
training procedure. The statistical parameters such as correlation
coefficient (R2), and mean square error (MSE) were also used to
evaluate the developed NNs model. The best number of neurons in
the middle layer of NNs model for methods (1) and (2) were
calculated 44 and 6, respectively. The R2 and MSE values of the test
phase were determined for method (1), 0.94 and 0.0016, and for
method (2), 0.98 and 0.00065, respectively, which shows that method
(2) estimates saturated hydraulic conductivity better than method (1).