FIRE RESISTANCE OF ENERGY EFFICIENT FLOOR STRUCTURES

This paper presents the numerically achieved results for the fire resistance of several types of floor structures which are mostly used in our residential and rural buildings and in same time fulfil the energy efficient criteria, as: semi-prefabricated reinforced concrete slabs system FERT and STIRODOM (with infill of extruded polystyrene -XPS), timber-concrete composite floor structure and traditional timber floor structure. The solid RC slab was analysed only for comparison. Using the computer programs SAFIR, the effect of the intensity of the permanent and variable actions and the effect of the thermal isolation on the fire resistance of simply supported slabs were analyzed. The fire resistance was defined with respect to the criteria of usability of the structures in fire conditions, according to Eurocodes and the standards in force

Application of artificial neural networks in civil engineering

Primjena umjetnih neuronskih mreža za rješavanje složenih građevinskih problema je od ogromne važnosti za proces projektiranja. One se mogu uspješno koristiti za prognostičko modeliranje u različitim inženjerskim područjima, osobito u onim slučajevima u kojima već postoje neka prethodna istraživanja (numerička ili pokusna). Ovaj rad prikazuje neke od pozitivnih aspekata modela neuronske mreže kad se koriste za utvrđivanje požarne otpornosti građevinskih elemenata.The application of artificial neural networks for solving complex civil engineering problems is of huge importance for the construction design process. They can be successfully used for prognostic modelling in different engineering fields, especially in those cases where some prior (numerical or experimental) analyses were already made. This paper presents some of the positive aspects of neural network’s model that was used for determination of fire resistance of construction elements

Application of artificial neural networks in civil engineering

Primjena umjetnih neuronskih mreža za rješavanje složenih građevinskih problema je od ogromne važnosti za proces projektiranja. One se mogu uspješno koristiti za prognostičko modeliranje u različitim inženjerskim područjima, osobito u onim slučajevima u kojima već postoje neka prethodna istraživanja (numerička ili pokusna). Ovaj rad prikazuje neke od pozitivnih aspekata modela neuronske mreže kad se koriste za utvrđivanje požarne otpornosti građevinskih elemenata.The application of artificial neural networks for solving complex civil engineering problems is of huge importance for the construction design process. They can be successfully used for prognostic modelling in different engineering fields, especially in those cases where some prior (numerical or experimental) analyses were already made. This paper presents some of the positive aspects of neural network’s model that was used for determination of fire resistance of construction elements

Fire-resistance prognostic model for reinforced concrete columns

U radu je prikazan prognostički model za određivanje požarne otpornosti armiranobetonskih stupova izloženih standardnom požaru sa svih strana. Predloženi model primjenjuje koncept umjetnih neuralnih mreža čiji su ulazni parametri rezultat prethodno provedene numeričke analize. Dan je kratak opis procesa modeliranja, kao i odgovarajući primjer primijenjenog prognostičkog modela.The prediction model used for defining fire resistance of reinforced concrete columns exposed to standard fire from all four sides is presented in the paper. The proposed model relies on the concept of artificial neural networks, in which numerical analysis results are used as input parameters. A brief description of the modelling process is given, and an appropriate example of the neural network prognostic model is presented

Mechanical properties of zeolit samples from Strmosh mine, Republic of Macedonia

The increased usage and exploitation of non-metals as a substitute for exploitation and processing of metals, is a trend in the world. Mechanical properties of the material are requisite for determining its application in civil engineering. This paper presents the strength characteristics of the zeolite, obtained from the Strmosh Mine, as well as its capability of water absorption. The procedure, as well as the results of the performed testing of the material are elaborated and presented, followed by a discussion about the obtained values

Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks

Artificial neural networks, in interaction with fuzzy logic, genetic algorithms, and fuzzy neural networks, represent an example of a modern interdisciplinary field, especially when it comes to solving certain types of engineering problems that could not be solved using traditional modeling methods and statistical methods. They represent a modern trend in practical developments within the prognostic modeling field and, with acceptable limitations, enjoy a generally recognized perspective for application in construction. Results obtained from numerical analysis, which includes analysis of the behavior of reinforced concrete elements and linear structures exposed to actions of standard fire, were used for the development of a prognostic model with the application of fuzzy neural networks. As fire resistance directly affects the functionality and safety of structures, the significance which new methods and computational tools have on enabling quick, easy, and simple prognosis of the same is quite clear. This paper will consider the application of fuzzy neural networks by creating prognostic models for determining fire resistance of eccentrically loaded reinforced concrete columns

Neural-network-based approach for prediction of the fire resistance of centrically loaded composite columns

Studije koje su dosad sprovedene pokazuju da se umjetne neuronske mreže mogu uspješno koristiti kao prognostički model u različitim inženjerskim područjima, posebice u onim slučajevima u kojima već postoje prethodna istraživanja i analize (numerički ili pokusni). U ovom radu prikazani su neki od pozitivnih aspekata njihove primjene za određivanje vatrootpornosti centrično opterećenih kompozitnih stupova od betona i čelika, izloženih vatri sa svih strana. Analize su provedene na tri različite vrste kompozitnih stupova: potpuno ubetonirani čelični profil, djelomično ubetoniranih čelični profil i šuplji profil od čelika ispunjen betonom. Utjecaj oblika, dimenzije presjeka i intenziteta aksijalne sile na požarnu otpornost centrično opterećenih kompozitnih stupova analizirani su pomoću programa FIRE. Rezultati provedenih numeričkih analiza korišteni su kao ulazni parametri za treniranje modela neuronske mreže koji je prilagođen za predviđanje vatrootpornosti centrično opterećenih kompozitnih stupova.The use of the neural-network-based approach, as an unconventional approach for solving complex civil engineering problems, has a huge significance in the modernization of the construction design processes. Worldwide studies show that artificial neural networks can be successfully used as prognostic model in different engineering fields, especially in those cases where some prior (numerical or experimental) analyses were already made. This paper presents some of the positive aspects of their application for determination the fire resistance of centrically loaded steel-concrete composite columns exposed to fire from all sides. The analyses were performed for three different types of composite columns: totally encased, partially encased and hollow steel sections filled with concrete. The influence of the shape, the cross sectional dimensions and the intensity of the axial force to the fire resistance of centrically loaded composite columns were analysed using the program FIRE. The results of the performed numerical analyses were used as input parameters for training the neural network model which is capable for predicting the fire resistance of centrically loaded composite columns