Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network (ANN)

The evaluation of structural performance of existing concrete buildings, built according to standards and materials quite different to those available today, requires procedures and methods able to cover lack of data about mechanical material properties and reinforcement detailing. To this end detailed inspections and test on materials are required. As a consequence tests on drilled cores are required; on the other end, it is stated that non-destructive testing (NDT) cannot be used as the only mean to get structural information, but can be used in conjunction with destructive testing (DT) by a representative correlation between DT and NDT. The aim of this study is to verify the accuracy of some formulas of correlation available in literature between measured parameters, i.e. rebound index, ultrasonic pulse velocity and compressive strength (SonReb Method). To this end a relevant number of DT and NDT tests has been performed on many school buildings located in Cesena (Italy). The above relationships have been assessed on site correlating NDT results to strength of core drilled in adjacent locations. Nevertheless, concrete compressive strength assessed by means of NDT methods and evaluated with correlation formulas has the advantage of being able to be implemented and used for future applications in a much more simple way than other methods, even if its accuracy is strictly limited to the analysis of concretes having the same characteristics as those used for their calibration. This limitation warranted a search for a different evaluation method for the non-destructive parameters obtained on site. To this aim, the methodology of neural identification of compressive strength is presented. Artificial Neural Network (ANN) suitable for the specific analysis were chosen taking into account the development presented in the literature in this field. The networks were trained and tested in order to detect a more reliable strength identification methodology.

Aquivion-based spray freeze-dried composite catalysts for the cascade conversion of furfural to γ-valerolactone

Furfural is one of the most promising biomass derived platform molecules. It is to this day produced in volumes above 300 ktons per year from the hydrolysis and dehydration of hemicellulose, one of the main components of lignocellulosic biomass. While the majority of the yearly production is destined to selective reduction to furfuryl alcohol for the production of furan resins, these molecules hold great potential for the production of more valuable chemicals, fuels, fuel additives and solvents. Among these products are alkyl levulinates and γ-valerolactone. To convert furfural to these target products, a cascade process involving Lewis acidity-catalysed reduction steps and Brønsted acidity-catalysed steps. In order to develop catalysts capable of promoting the one-pot domino reaction from furfural to γ-valerolactone, the two kinds of acidity must both be present. To this end, in this work, the spray freeze-drying technique is employed to combine the high activity and strong Brønsted acidity of Aquivion with the structural properties and Lewis acidity of different supporting metal oxide, forming composite catalysts. The flexibility of the spray freeze-drying technique and the modulable composition of the catalysts allowed a thorough study of the complex network of equilibria underlying the cascade reaction, while achieving high selectivities towards the final product.