A way to minimize uncertainty and achieve the best possible project performance in construction project management can be achieved during the procurement process, which involves selecting an optimal contractor according to “the most economically advantageous tender.” As resources are limited, decision-makers are often pulled apart by conflicting demands coming from various stakeholders. The challenge of addressing them at the same time can be modelled as a multi-criteria decision-making problem. The aim of this paper is to show that the analytic hierarchy process (AHP) together with PROMETHEE could cope with such a problem. As a result of their synergy, a decision support concept for selecting the optimal contractor (DSC-CONT) is proposed that: (a) allows the incorporation of opposing stakeholders’ demands; (b) increases the transparency of decision-making and the consistency of the decision-making process; (c) enhances the legitimacy of the final outcome; and (d) is a scientific approach with great potential for application to similar decision-making problems where sustainable decisions are needed.

A contractor’s ability to prepare a competitive bid for a construction tender is crucial for its survival on the market. The bid price estimation strategy should promote the probability of winning a sufficient amount of tenders but, at the same time, ensure the economic stability and development of the company. This paper aims to address this issue in the area of Czech public construction procurement. The opinions, experiences and practices of contractors were collected through a questionnaire survey, and the data were evaluated with the support of statistical methods. This revealed that Czech contractors mostly base their multicriteria bidding strategy on cost-oriented pricing while considering various aspects such as the risks and attractiveness of the tender. The Czech construction market is generally perceived as oriented toward low costs, and with a relatively common occurrence of abnormally low bids. The findings presented in this paper may help contractors improve their current bidding strategies in public construction procurement.

The maintenance planning within the urban road infrastructure management is a complex problem from both the management and technoeconomic aspects. The focus of this research is on decision‐making processes related to the planning phase during management of urban road infrastructure projects. The goal of this research is to design and develop an ANN model in order to achieve a successful prediction of road deterioration as a tool for maintenance planning activities. Such a model is part of the proposed decision support concept for urban road infrastructure management and a decision support tool in planning activities. The input data were obtained from Circly 6.0 Pavement Design Software and used to determine the stress values (560 testing combinations). It was found that it is possible and desirable to apply such a model in the decision support concept in order to improve urban road infrastructure maintenance planning processes.

Since complex urban systems are a combination of different spatial typologies and may have various complex characteristics (human, environmental, social, economic, political, and cultural), decision-making cannot be done without a holistic approach and complex analysis and evaluation of the possible development scenarios. This is especially true in today’s world, where spatial and urban planning must consider various objectives such as sustainable development, accessibility, landscape characteristics, and public interests on a strategic level. This results in the need to use appropriate decision support and decision-making tools in the strategic urban planning process of complex urban environments with Historic Urban Landscapes (HUL) characteristics to improve urban development and resilience. The proposed evaluation model, “Strategic Planning for Urban Resilience—SPUR model,” is based on a holistic approach to spatial planning with an emphasis on the strategic sustainable development of urban voids as HULs and was tested on the case study of the Delta area in the City of Rijeka, Croatia. The proposed holistic approach can help in scenario building, evaluation, and selection of planning alternatives, as well as decision-making processes in urban management, by enabling the evaluation and control of complex scenarios from a sustainable development perspective.

The oscillation of asphalt mix composition on a daily basis significantly affects the achieved properties of the asphalt during production, thus resulting in conducting expensive laboratory tests to determine existing properties and predicting the future results. To decrease the amount of such tests, a development of artificial neural network and multiple linear regression models in the prediction process of predetermined dependent variables air void and soluble binder content is presented. The input data were obtained from a single laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found that it is possible and desirable to apply such models in the prediction process of the HMA properties. The final aim of the research was to compare results of the prediction models on an independent dataset and analyze them through the boundary conditions of technical regulations and the standard EN 13108-21.