Design formula for the flexural strengthening of RC beams using prestressed CFRP reinforcement

Applying a certain prestress level to the carbon fiber reinforced polymer (CFRP) reinforcement according to either externally bonded reinforcing (EBR) or near surface mounted (NSM) techniques can mobilize the strengthening potentialities of this high tensile strength composite material. For the prediction of the flexural behavior of reinforced concrete (RC) structures strengthened with prestressed EBR or NSM CFRPs, however, simplified analytical and design formulations still need to be developed as a guidance for engineers to design this type of strengthened structures by hand calculation without any programming help. Hence, the current work aims to briefly explain a developed simplified analytical approach, with a design framework, to predict the flexural behavior of RC beams flexurally strengthened with either prestressed EBR or NSM CFRP reinforcements. Moreover, an upper limit for the prestress level is proposed in order to optimize the ductility performance of the NSM prestressing technique. The good predictive performance of the analytical approaches was appraised by simulating the results of experimental programs composed of RC beams strengthened with prestressed NSM CFRP reinforcements.

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.