Faculty of Civil Engineering

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Author: search.filters.author.Knezevic MilosLanguage: search.filters.language.English

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    Assessment of current state of Modernist building heritage of Skopje in terms of sustainability, energy efficiency and authentic appearance degradation
    (IOP Publishing, 2020)
    Dimevska Liljana
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    Cvetkovska Meri
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    Trombeva Gavriloska Ana
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    Lazarevska Marijana
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    Knezevic Milos
    Skopje has unique examples of modernist architecture that play a major role in creating the new identity of the city, which was completely lost after the earthquake in 1963. In the last decade, awareness has been raised that modernist buildings deserve to be protected as cultural monuments. On the other hand, concerning energy efficiency and sustainability aspect, they were built without thermal insulation and thus have extremely poor thermal properties. In order to improve the energy efficiency, retrofit interventions are constantly being implemented and their authentic appearance have been changed. Many buildings have already been changed to an unrecognizable stile which threatens the overall architectural identity of the city. Therefore, systematic research has been done on 50 valuable buildings from the period of Modernism in Skopje. The buildings are selected according to their architectural, structural and cultural historical values. They are analysed from following aspects: construction, building materials, thermal properties, function, cultural heritage protection, level of authenticity degradation, preservation and retrofit measures. The purpose of this paper is to define the most common types of buildings, which are in same time the most problematic in terms of energy efficiency, sustainability and authentic appearance.
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    Item type:Publication,
    REHABILITATION OF THE RAILWAY SECTION „KOS-TREBEŠICA“ IN MONTENEGRO
    (2020-03)
    Zafirovski Zlatko
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    Knezevic Milos
    ;
    Tesovic Ivana
    This paper gives a brief overview of the works performed on the rehabilitation of the railway section “Kos-Trebešica”. The section extends from km 351 + 684.65 to km 358 + 428.35 for a total length of 6.75 km. The complexity of this section is essentially determined by the fact that it comprises 12 tunnels and six bridges, which is more than 50% of the whole section. During the rehabilitation, works on the substructure (landslide rehabilitation) and the superstructure, as well as some minor works on the overhead contact line and telecommunications were carried out. Rehabilitation works with a duration of 4 hours and 30 minutes a day, were carried out in difficult working conditions. Carrying out the works so that every day after their completion, passenger and cargo traffic could proceed unhindered was a true engineering challenge. The technology of the works, the materials used, as well as problems encountered during the implementation of the project, will be described shortly. From the organizational point of view, the works on the section were made even more difficult by the fact that there are no access roads and no service. The Contractor was a consortium of JV Strabag AG Austria and Crnagoraput AD Podgorica. The supervision of the works was carried out by the Institute of Civil Engineering Ltd. Podgorica. The Contracting Authority was the Directorate of Public Works Podgorica, and the End Recipient the Railway Infrastructure of Montenegro. The value of the works is 5,379,205.22 EUR. The contract was formed in accordance with the Fidic Red Book (1999 edition).
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    Impact of thermal insulation and type of windows on energy demand of buildings with passive house standard
    (2015)
    Knezevic Milos
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    Trpevski Strahinja
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    Andreev Andrej
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    NEURAL-NETWORK-BASED APPROACH FOR PREDICTION OF THE FIRE RESISTANCE OF CENTRICALLY LOADED COMPOSITE COLUMNS
    (2016)
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    Knezevic Milos
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    Milanovic Milivoje
    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.
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    Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks
    (Hindawi, 2018-08-23)
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    Laban Mirjana
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    Knezevic Milos
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    Artificialneural networks,ininteraction withfuzzy logic, genetic algorithms,and fuzzy neuralnetworks, represent anexample ofa 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 prognosticmodeling fieldand,withacceptablelimitations,enjoyagenerallyrecognizedperspectiveforapplicationinconstruction. 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.