Ivan Marović

Coastal cliff geometry derived from structure-from-motion photogrammetry at Stara Baška, Krk Island, Croatia

The aim of this study was to examine the capability of structure-from-motion photogrammetry in defining the geometry of cliffs and undercuts in rocks of complex geomorphology. A case site was chosen along pocket beaches near the village of Stara Baška on the Adriatic Sea island of Krk, Gulf of Kvarner, Croatia, where cliff erosion of 5 m in breccias was identified by comparison of aerial photographs from 1960 and 2004. The 3D point cloud was derived from approx. 800 photos taken on 9 January 2014 by a single camera from various elevations and angles, and processed using the online software ReCap (Autodesk). Data acquisition was found to be quick and the method easy to implement. The difference between the georeferenced 3D cloud points and an RTK-GPS survey was 7 cm, i.e. within the limits of RTK-GPS precision. Quantifying the spatial variation in undercut geometries revealed that the deepest and largest (17 m3) undercut was in the south-eastern sector of the beach. Reconstructing the detailed geomorphology of this 3.8-m-deep undercut convincingly demonstrates the high efficiency of the method. Such assessments of spatiotemporal changes in undercut and overhang volumes can prove useful for evaluations of cliff erosion risk. Coupled with the low cost and relatively simple application, this is evidently an attractive technique for meaningful geotechnical and coastal engineering monitoring in the future on the island of Krk and, for that matter, also on other Adriatic islands and in similar settings worldwide.

Podrška odlučivanju u upravljanju prometnim projektima u urbanim sredinama

Faza planiranja unutar upravljanja urbanim prometnim projektom složen je proces kako sa stajalista menadžmenta tako i s tehnicko-ekonomskih stajalista. Ovo istraživanje usmjereno je na procese donosenja odluka vezanih za fazu planiranja prilikom upravljanja urbanim cestovnim infrastrukturnim projektima. Predloženi koncept se temelji na visekriterijskim metodama te na umjetnim neuronskim mrežama. U ovom radu predstavljeni koncept za podrsku odlucivanju testiran je na cestovnoj infrastrukturi grada Splita i prikazuje nacin na koji se može unaprijediti planiranje urbane cestovne infrastrukture.

Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

DECISION SUPPORT CONCEPT FOR MANAGING THE MAINTENANCE OF CITY PARKING FACILITIES

In this paper, we study the maintenance of city parking facilities (CPFs), specifically on modeling and support for decision-making related to planning CPF maintenance. Managing the maintenance of CPFs is complicated because it is a multi-disciplinary problem involving many participants, huge quantities of information, limited budgets, and conflicts of goals and criteria. These facts indicate that the decision-making processes in managing the maintenance of CPFs are ill-defined problems. To help maintenance managers cope with this complexity, we propose an approach that combines analytic hierarchy processing (AHP) and PROMETHEE multicriteria methods, and apply this approach to a priority-setting problem. After assessing the conditions of existing CPFs and the planned states of those CPFs, our approach produced a goal tree and criteria, and defined possible actions for the parking facilities. Representatives of stakeholders provided criteria weights by applying the AHP method. Using PROMETHEE II, we ranked the priorities, and the PROMETHEE V method allowed us to define the implementation phases of maintenance, producing the final maintenance plan. We validated our concept in the city of Split.

Development of ANN Model for Wind Speed Prediction as a Support for Early Warning System

The impact of natural disasters increases every year with more casualties and damage to property and the environment. Therefore, it is important to prevent consequences by implementation of the early warning system (EWS) in order to announce the possibility of the harmful phenomena occurrence. In this paper, focus is placed on the implementation of the EWS on the micro location in order to announce possible harmful phenomena occurrence caused by wind. In order to predict such phenomena (wind speed), an artificial neural network (ANN) prediction model is developed. The model is developed on the basis of the input data obtained by local meteorological station on the University of Rijeka campus area in the Republic of Croatia. The prediction model is validated and evaluated by visual and common calculation approaches, after which it was found that it is possible to perform very good wind speed prediction for time steps , , and . The developed model is implemented in the EWS as a decision support for improvement of the existing “procedure plan in a case of the emergency caused by stormy wind or hurricane, snow and occurrence of the ice on the University of Rijeka campus.“

Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

The oscillation of asphalt mix composition on a daily basis significantly 10 affects the achieved properties of the asphalt during production, thus resulting in 11 conducting expensive laboratory tests in order to determine existing properties and 12 predicting the future results. In order to decrease the amount of such tests, a 13 development of artificial neural network (ANN) and multiple linear regression (MLR) 14 models in the prediction process of predetermined dependent variables air void and 15 soluble binder content is presented. The input data were obtained from a single 16 laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found 17 that it is possible and desirable to apply such models in the prediction process of the 18 HMA properties. The final aim of the research was to compare results of the prediction 19 models on an independent dataset and analyse them through the boundary conditions of 20 technical regulations and the standard EN 13108-21. 21

Establishing a model to evaluate public administration projects

Public investment projects are projects in which the public interest is paramount. The public administration generates public projects, which can be grouped into portfolios. We often witness poor, disordered public investment and project selection. One reason is the lack of adequate, consistent methodology for evaluating and selecting projects. It can be difficult to choose from comparable projects within the portfolio, and the attitudes of the stakeholders can hinder decision-making, highlighting the need for multi- criteria analysis. To select the best project, we designed a model to evaluate and rank public administration projects. We presented a hierarchical structure of objectives and criteria for comparing projects. To select and compare projects according to these defined criteria and assigned weights, we used PROMETHEE II. We simulated and validated this model using the portfolio of the city of Kastav. This model contributes to higher quality and more consistent decision-making in selecting the best investment project from a public administration portfolio.

The contribution to the urban heat islands exploration: underpasses and their elements

ABSTRACT Final and other layers of road structures and their corresponding elements significantly affect the generation of heat islands in areas in which they are located Air temperature increases in relation to the surrounding areas during the formation of heat islands. This paper presents the results of field research which measured the temperature of all elements of two road underpasses, which are integral parts of the D7 (E73) state road in the city of Osijek, the Republic of Croatia. The tests were conducted from April to November 2014. The aim of the performed tests was to determine the effects of solar radiation on the temperature of constituent elements of road underpasses. The measurements were performed with the aim of determining the dependence of location of individual elements of the construction in space and material properties of which the same are made. The obtained test results indicate that the temperatures of the observed elements of the road underpasses significantly vary depending on the position within the construction, surface albedo and external weather conditions.

Urban Road Infrastructure Maintenance Planning with Application of Neural Networks

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.

Data Flow in Relation to Life-Cycle Costing of Construction Projects in the Czech Republic

Life-cycle costing is an important part of every construction project, as it makes it possible to take into consideration future costs relating to the operation and demolition phase of a built structure. In this way, investors can optimize the project design to minimize the total project costs. Even though there have already been some attempts to implement BIM software in the Czech Republic, the current state of affairs does not support automated data flow between the bill of costs and applications that support building facility management. The main aim of this study is to critically evaluate the current situation and outline a future framework that should allow for the use of the data contained in the bill of costs to manage building operating costs.