Continuous monitoring of sand-cement stiffness starting from layer compaction with a resonant frequency-based method: Issues on mould geometry and sampling

The application of a vibration-based methodology for the continuous measurement of the stiffness of sand–cement has recently been proposed by the authors of this work. Such methodology consists of placing the sand–cement sample into a mould, then placing the mould in simply supported conditions, and finally monitoring it over time to assess the evolution of its resonant frequency. This evolving resonant frequency of the system can be analytically correlated to the stiffness of the tested material. Based on the success of the pilot application, this work has been extended to the methodology of in situ sampling. Such an extension involves the use of new geometries and materials for the moulds. The performance of the adapted technique is verified by comparing its results to those obtained through uniaxial compression cyclic tests up to the age of 28 days. This work also encompasses the characterisation of the hydration kinetics of a cement paste, made with the same cement as that used for cementing sand, and draws conclusions about the relationship of stiffness evolution in both materials.Fundação para a Ciência e a Tecnologia (FCT

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