Abstract
Cement stabilisation is one of the commonly used
techniques to improve the strength of soft
ground/clays, generally found along coastal and low
land areas. The strength development in cement
stabilization technique depends on the soil properties,
cement content, curing period and environmental
conditions. For optimal and effective use of cement,
there is a need to develop a mathematical model
relating the gain in strength in terms of the variables
responsible. The existing empirical model in the
literature assumes linear variation of normalised
strength with the logarithm of curing period and hence,
different empirical models are required for different
conditions of the same soil. Also, the accuracy of
strength prediction is unsatisfactory. Due to unknown
functional relationships and nonlinearity in strength
development, in this paper the computational
intelligence techniques such as multilayer perceptron
(MLP), radial basis function (RBF) and genetic
programming (GP) are used to develop a mathematical
model. To generate the mathematical model, an
experimental study is conducted to obtain the strength
of three inland soils namely, red earth (CL), brown
earth (CH) and black cotton soil (CH) for different
water contents, cement contents and curing periods. In
order to generate a generic mathematical model using
computational intelligence techniques, two saline soils
(Ariake clay-3 and Ariake clay-4) and three inland
soils are used. A detailed study of the relative
performance of the computational intelligence
techniques and the empirical model has been carried
out.
Users
Please
log in to take part in the discussion (add own reviews or comments).