Abstract. The importance of performance based benchmarking has become a necessity in a modern construction company and presents a constant challenge for the construction industry. The aim of this paper is to elaborate significance, role and types of Key Performance Indicators (KPI) in the construction industry and show how different management perspectives perceive the indicators. A literature review was carried out in order to generate a listing of KPIs, used among academe and the industry. Afterwards, using surveys and semi-structured interviews, the data was gathered from more than 30 SouthEast European construction companies. Results were analyzed, producing a final set of 37 indicators. This study identified a low level of awareness of KPI models and performance management processes among the companies. Furthermore, the analysis showed a substantial difference in perception of KPIs among investors, consultants and contractors, which consequently led to a compiling list of KPIs. The top ten KPI's are: Quality, Cost, Number of investor interferences, Changes in project support, Time increase, Client satisfaction, Employees' satisfaction, Innovation and learning, Time and Identification of client's interest. The paper concludes with final remarks and guidelines for the implementation of KPIs in practice.

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The level of sensitivity to project success of large infrastructure projects is significantly greater in front-phase than in execution phase. Yet, due to focus on execution phase, methods for project assessment and on-going evaluation during front phases are insufficiently developed. On the other hand, risk management approaches has been moved from risk management towards holistic uncertainty management which is the most beneficial in front end phase of the project. This research identifies that majority of methods and techniques available does not support uncertainty management concept. The purpose of this paper is to develop and new method for risk based project assessment and evaluation inte­grating risk impact modelling using cumulative distribution curves (CDC) and multi-criteria project evaluation approach. Research is based on in-depth risk analysis of 15 large infrastructure projects using risk model of components and char­acteristics. The conclusion of the paper is verification and validation of method that combines qualitative and quantita­tive analysis using risk components, risk breakdown structure, AHP method and risk impact modelling using cumulative distribution curves (CDC) for internal and external risk based assessment and evaluation of large infrastructure projects.

With the increasing number of nearly zero-energy buildings (NZEB) due to increase of global awareness on climate change, the new concepts of design and control must be developed because of great NZEB dependency on detailing and multidisciplinary approach. This paper proposes a three-level gateway control method for NZEB project delivery by using digital representation of the building in building information modeling (BIM) environment. These controls (C1, C2 and C3) are introduced before three main phases of any project delivery—design phase, construction phase and handover. The proposed project control procedure uses black-box building energy modeling within the BIM environment, so the paper explores the reliability of one tool for direct energy modeling within the BIM-authoring software. The paper shows two types of validation tests with satisfactory results. This leads to conclusion that analyzed tool for energy simulation within BIM environment can be used in a way that is described in a proposed project control procedure. For further research it is proposed to explore reliability of tools for energy simulation connected to other BIM-authoring software, so this project control procedure could be independent of BIM-authoring software used in the paper.

Even though horizontally linear projects have low complexity schedules, they are still not successful in meeting planned time. The deadlines are mostly based on estimations done in front-end project development when limited data are available. Early time estimation models in literature rely on few variables and, almost in all cases, one of them is the estimated cost. Early cost estimations can significantly deviate from actual costs and thus lead to unreliable time estimation. Time estimation models based on neural network and other alternative methods require databases and software, which complicates the process of time estimation. The purpose of this paper is to bridge the gap of scarce time estimation models and unreliable time estimates by developing a new method for time estimation. This research has been done on one large sewer system project. The case study shows how to extract several continuous activities for a pipeline project chosen from a sewer system. Moreover, a new algorithm for the calculation of project duration is devised based on the existing equation related to the linear scheduling method, and this algorithm works with continuous activities. The new method for construction time estimation is based on the extraction of linear continuous activities, usage of the algorithm for identification of minimal buffer between activities, and calculation of the project duration. To verify the algorithm, this method is used on another pipeline project from a sewer system. The limitation is that this method can be used only for base estimation. Further research needs to be done to include uncertainties and risks in the method.