Abstract:
This research presents the development of an Interactive Database for different soil
parameters for different areas of Dhaka City based on Artificial Neural Networks
(ANN). Since it is expensive to obtain extensive information regarding soil
parameters of a region, ANN can be used to generalize soil data over threedimensional
space. Soil data from the existing soil reports are used to train a hierarchy
of artificial neural networks for this purpose. The study has been divided into two
phases. In the first phase of the study, soil reports have been collected from different
Government and Non-government organization to prepare a relational database based
on Microsoft Access Software which is readily available in most of the Personal
Computers. This database can be used effectively for the preliminary design of any
geotechnical structures. Artificial neural network has the capacity to map a very
complex relationship among different parameters of a complex phenomena. It can
generalize and interpolate the missing data in any possible direction. Thereby a
complete three dimensional geotechnical database of an area can be obtained. In the
second phase of the study, back-propagation neural networks has been used to
simulate soil strength parameters (SPT and Unconfined Compression Strength) In
three dimension using the data from the geotechnical database developed in the first
phase of the study. The variables used in the models are Topographical Information,
Depth, Specific Gravity (GS), Water Content, Dry Density, Percentage of Sand, Silt
and Clay, Liquid Limit and Plastic Limit.
The database contains 140 borehole data of Dhaka. The database can be
updated easily and data of any place of Bangladesh can be added. The training of the
ANN system is performed based on the available data of SPT, UCS and other
available soil parameters stored in the database. The modeling approach has been
found to be successful. The model predictions are convergent with the observed
results. It has been observed that water content and dry density have significant effect
on both SPT and UCS. The other soil parameters GS, %Sand, %Silt, %Clay, LL or PL
do not have individual effect on SPT and UCS, but together with other variables they
can be used to predict SPT and UCS up to a depth of 25ft. Additionally, %Sand, %Silt
and %Clay together with Topographical Information and Depth has been used for
prediction of SPT up to a depth of 100ft.
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The methodology and. application developed in this research can be extended
in many directions. A framework that integrates spatially enhanced GIS systems with
3D graphics representation using a shared database can be developed. Also similar
ANN models for predicting soil strength parameters for other areas of Bangladesh
can be developed.
