The use of artificial neural networks in adiabatic curves modeling

Adiabatic hydration curves are the most suitable data for temperature calculations in concrete hardening structures. However, it is very difficult to predict the adiabatic hydration curve of an arbitrary concrete mixture. The idea of modeling adiabatic temperature rise during concrete hydration with the use of artificial neural networks was introduced in order to describe the adiabatic hydration of an arbitrary concrete mixture, depending on factors which influence the hydration process of cement in concrete. The influence of these factors was determined by our own experiments. A comparison between experimentally determined adiabatic curves and adiabatic curves, evaluated by proposed numerical model shows that artificial neural networks can be used to predict adiabatic hydration curves effectively. This model can be easily incorporated in the computer programs for prediction of the thermal fields in young concrete structures, implemented in the finite element or finite difference codes. New adiabatic hydration curves with some other initial parameters of the concrete mixture can be easily included in this model in order to expand the range of suitability of artificial neural networks to predict the adiabatic hydration curves. (C) 2008 Elsevier B.V. All rights reserved

Rheological parameters of fresh concrete – comparison of rheometers

U radu se prikazuje usporedba reoloških parametara 26 različitih svježih betona mjerenih pomoću dva koaksijalna cilindrična reometra ConTec Viscometer 5 i ICAR Rheometer. Provedeno je i mjerenje konzistencije slijeganjem i konzistencije rasprostiranjem. Statističke analize dobivenih rezultata pokazuju dobru korelaciju između dva reometra za granicu tečenja i plastičnu viskoznost. Provedenim istraživanjem ustanovljena je jaka korelacija između granice tečenja i obradivosti te slaba korelacija između obradivosti i plastične viskoznosti.The comparison of rheological parameters for 26 different types of fresh concrete, measured with two co-axial cylinder rheometers ConTec Viscometer 5 and ICAR Rheometer, is presented in the paper. The consistency by slump test and flow table test was also measured. Statistical analyses of results show good correlation between the two rheometers for the yield stress and plastic viscosity. During this study, a strong correlation was established between the yield stress and workability, while the correlation is weak between the workability and plastic viscosity

Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks

Ultrasonic pulse velocity technique is one of the most popular non-destructive techniques used in the assessment of concrete properties. However, it is very difficult to accurately evaluate the concrete compressive strength with this method since the ultrasonic pulse velocity values are affected by a number of factors, which do not necessarily influence the concrete compressive strength in the same way or to the same extent. This paper deals with the analysis of such factors on the velocity-strength relationship. The relationship between ultrasonic pulse velocity, static and dynamic Young's modulus and shear modulus was also analyzed. The influence of aggregate, initial concrete temperature, type of cement, environmental temperature, and w/c ratio was determined by our own experiments. Based on the experimental results, a numerical model was established within the Matlab programming environment. The multilayer feed-forward neural network was used for this purpose. The paper demonstrates that artificial neural networks can be successfully used in modelling the velocity-strength relationship. This model enables us to easily and reliably estimate the compressive strength of concrete by using only the ultrasonic pulse velocity value and some mix parameters of concrete. (C) 2008 Elsevier B.V. All rights reserved

Possibilities of using the ultrasonic wave transmission method to estimate initial setting time of cement paste

In this paper, the applicability of the ultrasonic wave transmission method to estimate the initial setting time of an arbitrary cement paste is discussed. Ultrasonic pulse velocity measurements were fully automated and measured continuously. The Vicar Needle Test was used in order to determine the initial setting time of cement pastes. Different cement pastes were prepared in order to check the influence of the water/cement ratio, type of cement, curing temperature, cement fineness, and some clinker compositions, on the relationship between the initial setting time and ultrasonic pulse velocity. It was found that the initial setting time of an arbitrary cement paste can be estimated very accurately by the time the first inflection point appears on the ultrasonic pulse velocity curve. Moreover, it can be estimated quite accurately by the time the ultrasonic pulse velocity reaches a fixed value, close to the value of the ultrasonic pulse velocity in water

Comparison between two ultrasonic methods in their ability to monitor the setting process of cement pastes

This paper presents the comparison between ultrasonic wave transmission (USWT) method and ultrasonic wave reflection (USWR) method in their ability to monitor the setting process of cement pastes. The velocity of ultrasonic longitudinal waves and shear wave reflection coefficient were measured simultaneously on cement pastes with different hydration kinetics. Even though both methods are able to reliably monitor the hydration process and formation of structure of an arbitrary cement paste, they monitor the setting process in different ways. The relationship between the velocity of longitudinal waves and shear wave reflection coefficient can be simplified into three characteristic phases and the end of the first phase can be used to define the beginning of the setting process of cement paste. (C) 2009 Elsevier Ltd. All rights reserved

New numerical procedure for the prediction of temperature development in early age concrete structures

A new numerical model for the prediction of temperature development in young concrete structures is briefly presented. With the pre-program. adiabatic hydration curves, which are used to determine the internal heat generation, are calculated. An artificial neural networks approach is used for this purpose. Adiabatic hydration curves, which were included in the learning set, were determined by our own experiments, using the adiabatic calorimeter which uses air as the coupling media. The main program is implemented in the finite element code. This program allows concrete structure designers and contractors to quantify and evaluate the effects of some concrete initial parameters on the adiabatic hydration curves and corresponding temperature development at an arbitrary point in the concrete element. Some examples are also presented and discussed. (C) 2009 Elsevier B.V. All rights reserve

From structural polymorphism to structural metamorphosis of the coat protein of flexuous filamentous potato virus Y

The structural diversity and tunability of the capsid proteins (CPs) of various icosahedral and rod-shaped viruses have been well studied and exploited in the development of smart hybrid nanoparticles. However, the potential of CPs of the wide-spread flexuous filamentous plant viruses remains to be explored. Here, we show that we can control the shape, size, RNA encapsidation ability, symmetry, stability and surface functionalization of nanoparticles through structure-based design of CP from potato virus Y (PVY). We provide high-resolution insight into CP-based self-assemblies, ranging from large polymorphic or monomorphic filaments to smaller annular, cubic or spherical particles. Furthermore, we show that we can prevent CP self-assembly in bacteria by fusion with a cleavable protein, enabling controlled nanoparticle formation in vitro. Understanding the remarkable structural diversity of PVY CP not only provides possibilities for the production of biodegradable nanoparticles, but may also advance future studies of CP’s polymorphism in a biological context

Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks

Ultrasonic pulse velocity technique is one of the most popular non-destructive techniques used in the assessment of concrete properties. However, it is very difficult to accurately evaluate the concrete compressive strength with this method since the ultrasonic pulse velocity values are affected by a number of factors, which do not necessarily influence the concrete compressive strength in the same way or to the same extent. This paper deals with the analysis of such factors on the velocity-strength relationship. The relationship between ultrasonic pulse velocity, static and dynamic Young\u27s modulus and shear modulus was also analyzed. The influence of aggregate, initial concrete temperature, type of cement, environmental temperature, and w/c ratio was determined by our own experiments. Based on the experimental results, a numerical model was established within the Matlab programming environment. The multilayer feed-forward neural network was used for this purpose. The paper demonstrates that artificial neural networks can be successfully used in modelling the velocity-strength relationship. This model enables us to easily and reliably estimate the compressive strength of concrete by using only the ultrasonic pulse velocity value and some mix parameters of concrete. (C) 2008 Elsevier B.V. All rights reserved