The recent financial crisis in Europe has led to large reduction of public investment and urges the recession-hit countries to revisit their approach to new infrastructure projects initiation in light of the current budgetary constraints. This paper reviews the methodologies and practices implemented worldwide with the aim to achieve cost efficiency in infrastructure development and critically discuss the current approach to planning and designing of infrastructure projects in Greece. A number of transport infrastructure projects under tendering or construction, including urban express roads, urban development projects, cruise port, urban rail and airport, are presented and analyzed. The authors highlight the critical role of the implementation of appropriate tools and informed decision making in design and construction and present alternative solutions of enhanced cost efficiency for the required value for money to be achieved in each of the above projects.
Sound understanding of technical projects performance is very important at the preliminary design phase. A crucial element in the decision-making process for selection of the best alternative solution is reliable, early cost estimates. This becomes particularly significant in road tunnel construction where the construction cost is expressively high and subject to considerable cost overruns due to inherit risks of underground conditions. In this paper a structural equation model (SEM) for estimating the final construction cost of road tunnels is developed. In a comparative analysis of SEM with multiple regression analysis (MRA) and neural networks (NNs), SEM offers better results allowing the user to visually depict the paths of how several variables affect the cost of an underground project.
Purpose -The purpose of this paper is to present an artificial neural network (ANN) model that predicts earthmoving trucks condition level using simple predictors; the model's performance is compared to the respective predictive accuracy of the statistical method of discriminant analysis (DA). Design/methodology/approach -An ANN-based predictive model is developed. The condition level predictors selected are the capacity, age, kilometers travelled and maintenance level. The relevant data set was provided by two Greek construction companies and includes the characteristics of 126 earthmoving trucks. Findings -Data processing identifies a particularly strong connection of kilometers travelled and maintenance level with the earthmoving trucks condition level. Moreover, the validation process reveals that the predictive efficiency of the proposed ANN model is very high. Similar findings emerge from the application of DA to the same data set using the same predictors. Originality/value -Earthmoving trucks' sound condition level prediction reduces downtime and its adverse impact on earthmoving duration and cost, while also enhancing the maintenance and replacement policies effectiveness. This research proves that a sound condition level prediction for earthmoving trucks is achievable through the utilization of easy to collect data and provides a comparative evaluation of the results of two widely applied predictive methods.
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