Data mining approach for unconfined compression strength prediction of laboratory soil cement mixtures
DOI:
https://doi.org/10.24849/j.geot.2019.145.01Keywords:
Soil-cement mixtures, unconfined compression strength, data minig approach, sensitivity analysisAbstract
The prediction of the uniaxial compression strength (qu) of soil cement mixtures is of upmost importance for design purposes. This is done traditionally by laboratory tests which is time and resources consuming. In this paper it is presented a new approach to assess qu over time based on the high learning capabilities of Artificial Intelligence (AI) techniques. A database of 444 records, encompassing cohesionless to cohesive and organic soils, different binder types, mixture conditions and curing time, were used to train three models based on Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Multiple Regression (MRs). The results show a promising performance in qu prediction of laboratory soil cement mixtures, being the best results achieved with an average of SVMs and ANNs model (RR2=0,95). These models catch very well the major effects of the input variables water/cement ratio, cement content, organic matter content and curing time, which are known as key parameters in soil cement mixtures behavior.
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