Wang, O and Al-Tabbaa, A (2013) Preliminary model development for predicting strength and stiffness of cement-stabilized soils using artificial neural networks. Computing in Civil Engineering - Proceedings of the 2013 ASCE International Workshop on Computing in Civil Engineering. pp. 299-306.Full text not available from this repository.
This paper presents ongoing work on data collection and collation from a large number of laboratory cement-stabilization projects worldwide. The aim is to employ Artificial Neural Networks (ANN) to establish relationships between variables, which define the properties of cement-stabilized soils, and the two parameters determined by the Unconfined Compression Test, the Unconfined Compressive Strength (UCS), and stiffness, using E50 calculated from UCS results. Bayesian predictive neural network models are developed to predict the UCS values of cement-stabilized inorganic clays/silts, as well as sands as a function of selected soil mix variables, such as grain size distribution, water content, cement content and curing time. A model which can predict the stiffness values of cement-stabilized clays/silts is also developed and compared to the UCS model. The UCS model results emulate known trends better and provide more accurate estimates than the results from the E50 stiffness model. © 2013 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 Mar 2017 07:41|