A novel approach based on soft computing techniques for unconfined compression strength prediction of soil cement mixtures

The prediction of the uniaxial compression strength (qu) of soil cement mixtures is of up most importance for design purposes. This is done traditionally by extensive 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 data mining 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 support vector machines (SVMs), artificial neural networks (ANNs) and multiple regression. The results show a promising performance in qu prediction of laboratory soil cement mixtures, being the best results achieved with the SVM model (R2= 0.94) and with an average of SVM and ANN model (R2= 0.95), well reproducing the major effects of the input variables water/cement ratio, cement content, organic matter content and curing time, which are known as preponderant in soil cement mixtures behaviour.This work was supported by FCT - ‘‘Fundação para a Ciência e a Tecnologia’’, within ISISE, project UID/ECI/04029/2013, and within CIEPQPF, project EQB/UI0102/2014, aswell Project Scope: UID/CEC/00319/2013 and through the post-doctoral Grant fellowship with reference SFRH/BPD/94792/2013.This work was also partly financed by FEDER funds through theCompetitivity Factors Operational Programme - COMPETE and bynational funds through FCT within the scope of the projects POCI-01-0145-FEDER-007633, POCI-01-0145-FEDER-007043 and POCI-01-0145-FEDER-028382