Hybrid system for highway construction cost estimation in initial stages of project development
Abstract
Razvoj i proširenje mreže auto-puteva spada u red ključnih faktora koji omogućavaju brži ekonomski napredak u nerazvijenim i zemljama u razvoju. Donosioci odluka o velikim kapitalnim projektima, kada razmišljaju da li uopšte ući u izgradnju novog projekta autoputa, svoje odluke prevashodno baziraju na inicijalnim procenama troškova izgradnje i očekivanim koristima od te izgradnje. Međutim, baš u početnim fazama investicionog projekta, prilikom izrade inicijalnih procena troškova, investitori i potencijalni izvođači radova nailaze na brojne izazove. Najveći izazov su vrlo oskudne informacije o karakteristikama budućeg auto-puta i nedostupnost ili mali broj podataka o troškovima prethodno realizovanih projekata, a na osnovu kojih bi se doneli zaključci o očekivanim troškovima realizacije budućih projekata...The development and expansion of the highway network is one of the key factors that enable faster economic growth in underdeveloped and developing countries. Decision makers for large capital projects, when considering whether to enter or not into construction of a new highway project, primarily base their decisions on initial construction cost estimates and expected benefits from that construction. However, in the initial stages of an investment project, owners and potential contractors encounter numerous challenges when making initial cost estimates. The biggest challenges are very scarce information on the characteristics of the future highway and the unavailability or small amount of data on the costs of previously implemented projects, based on which conclusions could be made about the expected costs of implementing future projects..- doctoralThesis
- donošenje odluka, procena troškova, troškovni parametri, rane faze projekta, projekti auto-puteva, mašinsko učenje, ekstremno gradijentno pojačavanje (XGBoost), veštačke neuronske mreže (ANN), višestruka regresiona analiza (MRA)
- decision-making, cost estimation, cost drivers, early project phases, highway projects, machine learning, eXtreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANN), Multiple Regression Analysis (MRA)