Influence of type of binder and sand on the characteristics of masonry mortars

This study deals with the characterization of masonry mortars produced with different binders and sands. Several properties of the mortars were determined, like consistence, compressive and flexural strengths, shrinkage and fracture energy. By varying the type of binder (Portland cement, hydrated lime and hydraulic lime) and the type of sand (natural or artificial), it was possible to draw some conclusions about the influence of the composition on mortars properties. The results showed that the use of Portland cement makes the achievement of high strength classes easier. This was due to the slower hardening of lime compared with cement. The results of fracture energy tests showed much higher values for artificial sand mortars when compared with natural sand ones. This is due to the higher roughness of artificial sand particles which provided better adhesion between sand and binder.

Exploring the potentialities of a new type of CFRP laminate for the simultaneous flexural and shear strengthening of RC beams: numerical research

This study aims to develop an innovative carbon fibre reinforced polymer (CFRP) laminate with a U configuration to address strengthening interventions, where the increment of both flexural and shear capacity of reinforced concrete (RC) elements is required. This strengthening solution combines the near surface mounted (NSM) and embedded through section (ETS) techniques in the same application, since these techniques have already evidenced high performance on flexural and shear strengthening of RC beams using FRP systems, respectively. In fact, the proposed hybrid technique aims to mobilize the advantages provided by these two strengthening techniques by using an innovative CFRP laminate. The strengthening efficacy of this new hybrid NSM/ETS technique was numerically assessed and compared to the corresponding efficiency of NSM and ETS techniques applied separately for the flexural and shear strengthening of RC beams, respectively. The numerical models are described and the main relevant results are presented and discussed.

Predicting type of delivery by identification of obstetric risk factors through data mining

In Maternity Care, a quick decision has to be made about the most suitable delivery type for the current patient. Guidelines are followed by physicians to support that decision; however, those practice recommendations are limited and underused. In the last years, caesarean delivery has been pursued in over 28% of pregnancies, and other operative techniques regarding specific problems have also been excessively employed. This study identifies obstetric and pregnancy factors that can be used to predict the most appropriate delivery technique, through the induction of data mining models using real data gathered in the perinatal and maternal care unit of Centro Hospitalar of Oporto (CHP). Predicting the type of birth envisions high-quality services, increased safety and effectiveness of specific practices to help guide maternity care decisions and facilitate optimal outcomes in mother and child. In this work was possible to acquire good results, achieving sensitivity and specificity values of 90.11% and 80.05%, respectively, providing the CHP with a model capable of correctly identify caesarean sections and vaginal deliveries.