Offer preparation has always been a specific part of a building process which has significant impact on company business. Due to the fact that income greatly depends on offer’s precision and the balance between planned costs, both direct and overheads, and wished profit, it is necessary to prepare a precise offer within required time and available resources which are always insufficient. The paper presents a research of precision that can be achieved while using artificial intelligence for estimation of cost and duration in construction projects. Both artificial neural networks (ANNs) and support vector machines (SVM) are analysed and compared. The best SVM has shown higher precision, when estimating costs, with mean absolute percentage error (MAPE) of 7.06% compared to the most precise ANNs which has achieved precision of 25.38%. Estimation of works duration has proved to be more difficult. The best MAPEs were 22.77% and 26.26% for SVM and ANN, respectively.

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Abstract. Paper is a brief review of the research focused on formulation an numerical model for the concrete pavement which is made by the recycling material. For numerical modeling the finite element model (FEM) and the 3D finite element model were applied. The software EverFE 2.25, was used. The results of FEM analysis is in a chapter shape showing move value change, strees and deflections for all layers a construction road model. In the next phase of the research was provided by FEM software with appropriate general purpose non-linear models, which allows the analysis of the real behavior of solid pavement under load.

This paper describes the physical, chemical and mineral properties of ash and slag, which were taken from thermal power plants Nikola Tesla A, Nikola Tesla B, Kostolac A and Kostolac B. The knowledge of the mineralogical material composition is important because the type of minerals directly determines the properties of the fly ash and slag and their possible application. Laboratory tests showed that ash and slag samples consist of the following minerals: amorphous materials, quartz, feldspar, mullite, melilite, cristobalite, haematite and calcite. The fly ash and slag chemical properties are the most important indicators in the evaluation of their suitability as a building material. The ash and slag chemical composition is composed of the following chemical components:

Preliminary noteEarlier researches have established that the measurement of pavement longitudinal roughness expressed through International Roughness Index (IRI) is one of the most important indicators for overall evaluation of road network condition. At the same time IRI presents the key trigger for planning and applying the different road maintenance works like pavement rehabilitation or reconstruction. This paper examines the existing methods for measurement of pavement roughness and evaluation of the road network condition in the Republic of Serbia in total length of 13 191,34 km. Keywords: condition, pavement, road network, roughnessMjerenje hrapavosti kolnika kao indikator stanja cestovne mreže -studija slučaja Srbija Prethodno priopćenje Prethodna istraživanja su utvrdila da je mjerenje uzdužne hrapavosti kolnika izraženo kroz međunarodni indeks hrapavosti (International Roughness Index -IRI) jedan od najvažnijih pokazatelja za ukupnu ocjenu stanja cestovne mreže. U isto vrijeme IRI predstavlja ključni okidač za planiranje i primjenu različitih radova održavanja cesta kao što su sanacije ili rekonstrukcije kolnika. U radu se razmatraju postojeće metode za mjerenje hrapavosti kolnika i ocjenu stanja cestovne mreže u Republici Srbiji u ukupnoj dužini od 13 191,34 km.