Shrestha, R and Al-Tabbaa, A (2012) Development of predictive models for cement stabilized soils. Geotechnical Special Publication. pp. 221-230. ISSN 0895-0563Full text not available from this repository.
Factors that affect the engineering properties of cement stabilized soils such as strength are discussed in this paper using data on these factors. The selected factors studied in this paper are initial soil water content, grain size distribution, organic matter content, binder dosage, age and curing temperature, which has been collated from a number of international deep mixing projects. Some resulting correlations from this data are discussed and presented. The concept of Artificial Neural Networks and its applicability in developing predictive models for deep mixed soils is presented and discussed using a subset of the collated data. The results from the neural network model were found to emulate the known trends and reasonable estimates of strength as a function of the selected variables were obtained. © 2012 American Society of Civil Engineers.
|Divisions:||Div D > Geotechnical and Environmental|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 18:42|
|Last Modified:||23 Jan 2017 05:34|