Estimating investment value in railway lines reconstruction process

U radu je prikazan prognostički model za procjenu investicijskih vrijednosti rekonstrukcije željezničkih pruga izrađen primjenom umjetnih neuronskih mreža. Cilj izrade modela je poboljšanje efikasnosti i efektivnosti donošenja odluka o ulaganju u projekte željezničke infrastrukture. Prikazan je proces izrade modela i dan je odgovarajući primjer kojim se upućuje na mogućnost primjene modela za eventualne grube i brze procjene investicijskih vrijednosti za rekonstrukciju željezničkih pruga s pouzdanošću od 80 do 85 % kada nisu poznati svi ulazni parametri.A prognostic model for estimating investment value of reconstruction of railway lines using artificial neural networks is presented in the paper. The aim of the model is to improve the efficiency and effectiveness of decision making as related to investment in rail infrastructure projects. The model development process is presented and illustrated with an appropriate example, which points to the possibility of using the model for making a rough and rapid assessment of the investment value of railway-lines reconstruction, with a reliability of 80-85% when some input parameters are unknown

Estimating investment value in railway lines reconstruction process

U radu je prikazan prognostički model za procjenu investicijskih vrijednosti rekonstrukcije željezničkih pruga izrađen primjenom umjetnih neuronskih mreža. Cilj izrade modela je poboljšanje efikasnosti i efektivnosti donošenja odluka o ulaganju u projekte željezničke infrastrukture. Prikazan je proces izrade modela i dan je odgovarajući primjer kojim se upućuje na mogućnost primjene modela za eventualne grube i brze procjene investicijskih vrijednosti za rekonstrukciju željezničkih pruga s pouzdanošću od 80 do 85 % kada nisu poznati svi ulazni parametri.A prognostic model for estimating investment value of reconstruction of railway lines using artificial neural networks is presented in the paper. The aim of the model is to improve the efficiency and effectiveness of decision making as related to investment in rail infrastructure projects. The model development process is presented and illustrated with an appropriate example, which points to the possibility of using the model for making a rough and rapid assessment of the investment value of railway-lines reconstruction, with a reliability of 80-85% when some input parameters are unknown

The evaluation of alternative solutions for the highway route E-763 Belgrade – South Adriatic: a case study of Serbia

Projektiranje putova predstavlja složen proces donošenja odluka čiji osnovni cilj treba biti realizacija projekta puta i njegova eksploatacija u užem smislu, ali i omogućavanje mobilnosti, privrednog razvoja područja i poboljšanje kvaliteta života u širem smislu. Sve ovo zahtjeva sagledavanje i razumijevanje mnogih problema koji su višekriterijske prirode i donošenje odluka u vezi sa tehničkim komponentama, ograničenjima okruženja i utjecajima na društvo. Osnovni cilj rada je da se na realnom primjeru objasni uloga i značaj metoda višekriterijskog vrednovanja. Prezentirane su teorijske postavke višekriterijskog vrednovanja. Primjenom metode višekriterijskog vrednovanja (metoda VIKOR) izvršeno je rangiranje ponuđenih alternativnih rešenja trase autoputa E-763 Beograd-Južni Jadran (dionica Požega-Boljare). Rangiranje je izvršeno na osnovu 12 kriterija koji čine osnovu vrednovanja svakog alternativnog rešenja. Proračun je izveden primjenom programskog paketa VIKOR i izvršena je analiza dobivenih rezultata.Designing road projects involves a complex decision-making process whose objectives should be the implementation of the road design and its utilization in the narrowest sense, but also the facilitation of mobility, economic development of the area and improvement of the quality of life in a wider sense. All of this requires the consideration and understanding of many problems multi-criterial in nature, and decision making with regard to technical components, environmental constraints and the impact on society. The main goal of this paper is to use a real example to explain the role and significance of multi-criteria evaluation methods. The theoretical postulates of multi-criteria evaluation are presented (VIKOR method). Using multi-criteria evaluation methods ranking was carried out of the alternative solutions offered for the E-763 highway route Belgrade-South Adriatic (Požega-Boljare section). Ranking was carried out on the basis of 12 criteria which form the basis for evaluating each of the alternative solutions. The calculation was performed using the VIKOR program packages and an analysis of the results obtained was carried out

Decision-Support System for Estimating Resource Consumption in Bridge Construction Based on Machine Learning

The paper presents and analyzes the state-of-the-art machine learning techniques that can be applied as a decision-support system in the estimation of resource consumption in the construction of reinforced concrete and prestressed concrete road bridges. The formed database on the consumption of concrete in the construction of bridges, along with their project characteristics, was the basis for the formation of the assessment model. The models were built using information from 181 reinforced concrete bridges in the eastern and southern branches of Corridor X in Serbia, with a value of more than 100 million euros. The application of artificial neural network models (ANNs), models based on regression trees (RTs), models based on support vector machines (SVM), and Gaussian processes regression (GPR) were analyzed. The accuracy of each model is determined by multi-criterion evaluation against four accuracy criteria root mean square error (RMSE), mean absolute error (MAE), Pearson’s linear correlation coefficient (R), and mean absolute percentage error (MAPE). According to all established criteria, the model based on GPR demonstrated the greatest accuracy in calculating the concrete consumption of bridges. According to the study, using automatic relevance determination (ARD) covariance functions results in the most accurate and optimal models and also makes it possible to see how important each input variable is to the model’s accuracy

Bone microRNA-21 as surgical stress parameter is associated with third molar postoperative discomfort

Objective: To evaluate an association between bone levels of inflammation/oxidative stress mediators and postoperative discomfort after third molar conventional or piezosurgery. Material and methods: Twenty-six subjects with bilaterally impacted mandibular third molars, who underwent either piezo or conventional surgery, were included in a split-mouth design study. MicroRNA-21 (miR-21) expression, interleukin-1 beta (IL-1β), and vascular endothelial growth factor (VEGF) proteins, as well as superoxide dismutase (SOD) activity in alveolar bone, were evaluated. Pain intensity, the first pain appearance, analgesic first use and total dose taken, trismus, and swelling were clinically recorded. Results: MiR-21 expression was higher while VEGF protein was lower in piezosurgery vs. conventional groups. The differences in IL-1β protein and SOD activity were not significant between groups. The pain intensity on the first day was significantly decreased in piezosurgery group. The first pain appearance and the first analgesic taken were reported sooner in conventional vs. piezosurgical group. Significantly pronounced trismus on the third day following conventional surgery was found. In conventional group, significantly increased trismus was observed on the third compared to the first postoperative day. MiR-21 showed significant correlation with the first pain appearance. Conclusion: Delayed onset of less pronounced postoperative pain after piezosurgical vs. conventional extraction of impacted lower third molar was significantly associated with expression of bone miR-21. Clinical relevance: Alveolar bone miR-21 may reflect surgical stress and is associated with third molar postoperative pain onset

An attempt to create a standardized (reference) model for experimental investigations on implant's sample

The aim of this study was to determine, evaluate and measure strain of vertically loaded implant using the digital image correlation method. The Straumann (R) dental implant system with the SLActive (R) surface was used in this study. Implant was immersed in poly-methyl-methacrylate during his hardening process. After preparation procedure a sample of implant and poly-methyl-methacrylate was obtained. This sample was loaded using external load from 0 to 400 N. Maximum strain in the 4 mm surface layer was 0.30% whereas maximum strain in the 6 mm surface layer (opposite side) was 0.20%, detected in the marginal and apical part of implant-poly-methyl-methacrylate sample. Minimum strain measured by Aramis software was 0.01%, detected in the 4 mm surface layer. According to results obtained by Aramis data processing, the 4 mm surface layer indicated higher overall strain in apical direction with the strains of 0.18-0.21%. Increasing the load did not affect the value of maximum strain however, higher load influenced the overall strain concentration increased especially in marginal and apical part of the sample surfaces that surrounded lateral sides of implant body

An attempt to create a standardized (reference) model for experimental investigations on implant's sample

The aim of this study was to determine, evaluate and measure strain of vertically loaded implant using the digital image correlation method. The Straumann (R) dental implant system with the SLActive (R) surface was used in this study. Implant was immersed in poly-methyl-methacrylate during his hardening process. After preparation procedure a sample of implant and poly-methyl-methacrylate was obtained. This sample was loaded using external load from 0 to 400 N. Maximum strain in the 4 mm surface layer was 0.30% whereas maximum strain in the 6 mm surface layer (opposite side) was 0.20%, detected in the marginal and apical part of implant-poly-methyl-methacrylate sample. Minimum strain measured by Aramis software was 0.01%, detected in the 4 mm surface layer. According to results obtained by Aramis data processing, the 4 mm surface layer indicated higher overall strain in apical direction with the strains of 0.18-0.21%. Increasing the load did not affect the value of maximum strain however, higher load influenced the overall strain concentration increased especially in marginal and apical part of the sample surfaces that surrounded lateral sides of implant body