BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Deep Soil Mixing and Predictive Neural Network Models - Ms Rakshya
  Shrestha\, CUED
DTSTART:20120316T160000Z
DTEND:20120316T170000Z
UID:TALK35635@talks.cam.ac.uk
CONTACT:Anama Lowday
DESCRIPTION:The work is basically a fusion of two existing research areas:
  Deep Soil Mixing (DSM) and Artificial Neural Networks (ANNs). The strengt
 h of the deep soil mixed walls and columns depends on a large number of fa
 ctors which vary in a wide range and the dependence is complex. Soil type\
 , grain size distribution\, water content\, plasticity index\, liquidity i
 ndex\, clay content\, organic matter content\, binder type\, water to bind
 er ratio\, mixing and curing conditions are few of the many variables that
  affect the strength development. The variability and uncertainty associat
 ed with these variables affect the magnitude of strength making the estima
 tion of strength not a very straight forward task. \nTo investigate this v
 ariability\, data on a large number of these variables has been collated i
 nto a database from a large number of DSM projects worldwide. Uniformity a
 nd variability in the strength data have been analysed based on the data f
 rom these sources. The effect of parameters such as water content\, liquid
 ity index\, cement content\, total water to cement ratio and curing times 
 on the UCS of the binder mixed soils has been studied by plotting these da
 ta together. The study has highlighted the importance of the variables suc
 h as liquidity index and total water to binder ratio which have not been e
 xtensively explored in previous DSM studies. \nPredictive Neural Network m
 odels which predict the strength gain of cement-stabilized clays as a func
 tion of clay water content\, liquidity index\, plasticity index\, organic 
 matter content\, grain size distribution\, cement content\, total water to
  cement ratio and curing time have been developed. Results from the neural
  network models were found to emulate the known trends and reasonable esti
 mates of strength as a function of the selected variables were obtained. T
 he effectiveness of these data-driven non-linear predictive models is disc
 ussed.\n
LOCATION:Engineering Department - Lecture Room 3B
END:VEVENT
END:VCALENDAR