Abstract. High-strength concrete is undoubtedly one of the most innovative materials in construction. Its manufacture is simple and is carried out starting from essential components (water, cement, fine and aggregates) and a number of additives. Their proportions have a high influence on the final strength of the product. This relations do not seem to follow a mathematical formula and yet their knowledge is crucial to optimize the quantities of raw materials used in the manufacture of concrete. Of all mechanical properties, concrete compressive strength at 28 days is most often used for quality control. Therefore, it would be important to have a tool to numerically model such relationships, even before processing. In this aspect, artificial neural networks have proven to be a powerful modeling tool especially when obtaining a result with higher reliability than knowledge of the relationships between the variables involved in the process. This research has designed an artificial neural network to model the compressive strength of concrete based on their manufacturing parameters, obtaining correlations of the order of 0.94.

ResumenSe proponen dos modelos de redes neuronales artificiales para la predicción del resultado del ensayo a compresión de un concreto de construcción tras el periodo de curado a partir de datos de fabricación fácilmente medibles. La resistencia a compresión del concreto es uno de los parámetros más importantes en su control de calidad. Sin embargo, estos ensayos se realizan tras un periodo de curado que hace que los resultados disten de ser inmediatos a la fabricación del producto. Por lo tanto, se propone un modelo matemático fiable para obtener los resultados del ensayo en forma inmediata. Los modelos propuestos presentan coeficientes de correlación mayores a 0.9 y permiten reducir considerablemente el tiempo en obtener los resultados de la resistencia a compresión. Palabras clave: resistencia a compresión, periodo de curado, redes neuronales artificiales, concreto, control de calidad. Use of Artificial Neural Networks for Modeling of the Test of Compressive strength of Construction Concrete According to the Standard ASTM C39/C 39 M AbstractTwo artificial neural network models for predicting the results of compressive strength test of a construction concrete after the curing period are proposed. The compressive strength of concrete is one of the most important variables in its quality control. However, these tests are carried out after a period of curing so results of the test are not immediately available. Therefore a reliable mathematical model that would obtain the test results immediately after the curing time These models present correlation coefficients higher than 0.9 and allow reducing the time to obtain the results of compressive strength tests.

Wooden bars arranged in cantilever configuration are put in oscillation. LPG fiber grating is used to register the flexural movement of the bar as a function of time to obtain the bar material’s Young’s modulus.

We present here the results of experimental research on the speed of absorption of CO2 by Portland cement pastes and pure and additive mortars. The samples, introduced in a chamber, are exposed to a high concentration of CO2, simultaneously monitoring the temperature, relative humidity, pressure and concentration of CO2 present inside. The results show greater rapidity of carbonation both in cement pastes and in mortars when they are added with plasticizers, air incorporators or workability additives.

In this investigation the method of self-curing of concrete is applied using polyethylene glycol (PEG 400), with the aim of proposing an innovative alternative of curing concrete that suppresses the traditional external curing of the concrete indicated in ASTM C31, in order to obtain expected compression strength results, observing the development of concrete hydration processes. The experimental campaign included the production of 157 cylindrical specimens of dimensions 100×200 mm and 18 beams of dimensions 150×150×500 mm. Water-cement (w/c) ratios =0.70, 0.60 and 0.45 and strength tests at 7, 14 and 28 days were considered. Dosages of PEG 400 were used in 0.5%, 1% and 1.5% of the cement weight for the determination of the dosage that provides the best compressive strength results. Concrete properties were characterized such as splitting tensile strength, modulus of rupture, and its microscopic composition was observed using Scanning Electron Microscopy (SEM). Finally, it was obtained that at 28 days, the dosage of 1% of the cement weight for PEG 400, provides the most satisfactory results of compressive strength, splitting tensile strength and modulus of rupture for w/c = 0.70, 0.60 and 0.45.