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SUMMARY:Data Mining\, Mapping and Modelling of the Strength of Cement-Stab
 ilised Soils - Ouge Wang
DTSTART:20131023T150000Z
DTEND:20131023T160000Z
UID:TALK47397@talks.cam.ac.uk
CONTACT:Jen Fusiello
DESCRIPTION:Cement stabilisation has been widely used for improving the en
 gineering properties of soft soils. The unconfined compressive strength (U
 CS) is the most common strength parameter used for the current design prac
 tice of cement-stabilised soil due to its simplicity and cost-effectivenes
 s. However the UCS test does not take into account the effect of confining
  stress on material strength\, and thus it is considered to be conservativ
 e\, variable and lacking in reliability. The undrained triaxial compressio
 n test\, although less straightforward to conduct and more costly\, is mor
 e representative in terms of simulating actual field conditions. The first
  part of this work presents data collection and collation from six researc
 h-based projects involving both UCS tests and undrained triaxial tests\, p
 erformed on the same laboratory-prepared cement-treated soil samples. Resu
 lts from the UCS tests were compared and correlated to those from the undr
 ained triaxial tests by normalising the data and developing contour plots 
 to illustrate the relationships between the two strengths. A Bayesian neur
 al network model was developed to provide better estimation of the strengt
 h correlations as a function of aforementioned input parameters. In order 
 to further explore the applicability of artificial neural network modellin
 g for cement-stabilised soils\, the second part of the study extends its a
 pplication to predicting the UCS and stiffness of stabilised soils. The co
 rrelations between stiffness and strength from both the UCS test and undra
 ined triaxial compression test were also studied as part of the work. Rela
 tionships between the UCS and stiffness for laboratory-stabilised soils an
 d field deep mixing were found to be consistent with the findings from exi
 sting literature. The overall research highlighted the potential of using 
 artificial intelligence for providing preliminary design parameters of cem
 ent-stabilised soils.
LOCATION:Engineering Department - Lecture Room 4
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