Prediction of Elastic Young Modulus over Time of Jet Grouting Laboratory Formulations by Application of Data Mining Techniques

The work present in this paper was developed under the PhD thesis entitled “Application of Data Mining Techniques to the Design of Jet Grouting Columns”. The main goals of this study are the developing of analytical model, by application of Data Mining (DM) techniques, able to estimate the mechanical and physical properties of Jet Grouting (JG) material, namely the evolution of uniaxial compressive strength and elastic Yong modulus over time, as well as the diameter of JG columns. Thus, in this paper, and as a part of the PhD study three Data Mining models, i.e. Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Functional Networks (FN) were used to estimate the Elastic Young Modulus (E0) of JG laboratory formulation over time. Furthermore, the results were compared with the Eurocode 2 analytical model.