High Performance of Silica Fume Mortars for Ferrocement Applications

The current study deals with obtaining the high performance mortars to use in the applications of ferrocement. The main problem that has greatly affected the performance of mortar is the workability. A low water-cement ratio mostly resulted in increases in the compressive strength and led to the enhancement of durability characteristics, but decreases in the workability. Workability becomes an important factors, as the mortar has to easily penetrate between the layers of the mesh wires. A reasonably workable with high strength cement mortar can be obtained by using a high cement content coupled with the use of silica fume and superplasticizers. In this investigation a series of compression tests were conducted on 50 mm cube and 150 ×300 mm, cylindrical specimens to obtain the compressive strength and the stress-strain behavior of mortar with silica fume and superplasticizers and flexural tests were conducted on 50 ×5 0 × 200 mm prism to obtain the modulus of rupture. The results of this study indicated that the variation in mortar strength depend on the water-to-binder ratio of the mix and percentages of cement replacing. The effects of these parameters on the stress-strain curves are presented. The best replacement percentage of silica is 3% was concluded in this study. From the experimental results a mathematical model has been developed to predict the 28-day compressive strength of silica fume mortar with different water-tocementitious ratios and superplasticizers percentag

Using data mining algorithms to predict the bond strength of NSM FRP systems in concrete

This paper presents the effectiveness of soft computing algorithms in analyzing the bond behavior of fiber reinforced polymer (FRP) systems inserted in the cover of concrete elements, commonly known as the near-surface mounted (NSM) technique. It focuses on the use of Data Mining (DM) algorithms as an alternative to the existing guidelines’ models to predict the bond strength of NSM FRP systems. To ease and spread the use of DM algorithms, a web-based tool is presented. This tool was developed to allow an easy use of the DM prediction models presented in this work, where the user simply provides the values of the input variables, the same as those used by the guidelines, in order to get the predictions. The results presented herein show that the DM based models are robust and more accurate than the guidelines’ models and can be considered as a relevant alternative to those analytical methods

Compressive strength of masonry made of clay bricks and cementmortar: Estimation based on Neural Networks and Fuzzy Logic

The use of mathematical tools such as Artificial Neural Networks and Fuzzy Logic has been shown to be useful for solving complex engineering problems, without the need to reproduce the phenomenon under study, when the only information available consists of the parameters of the problem and the desired results. Based on a collection of 96 laboratory tests, this paper uses Artificial Neural Networks and Fuzzy Logic to determine the compressive strength of a masonry structure composed of clay bricks and cement mortar, by using only two parameters: the compressive strength of the mortar and that of the bricks. These mathematical techniques are an alternative to the complex analytical formulas dependent on a large number of parameters and to empirical formulas, which, even though simple, often give unrealistic values. The results obtained are compared to the calculation methods proposed by other authors and other standards and demonstrate the suitability of using Neural Networks and Fuzzy Logic to predict the compressive strength of masonry. (C) 2012 Elsevier Ltd. All rights reserved.Garzón Roca, J.; Obrer Marco, C.; Adam Martínez, JM. (2013). Compressive strength of masonry made of clay bricks and cementmortar: Estimation based on Neural Networks and Fuzzy Logic. Engineering Structures. 48:21-27. doi:10.1016/j.engstruct.2012.09.029S21274