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In the new hall of the Institute for Materials and Structures, there was a need to control the geometry of the existing crane rails. In this paper, the methodology for determining the geometry of such a complex structure is presented, and the results of the applied method are also presented. The final product, i.e. the examined geometry of crane rails included numerical and graphical analysis of straightness, analysis of parallelism and analysis of horizontality of crane rails.

The paper describes the methodology for selecting the optimal solution for the design of the geodetic GNSS network for the Mandici landslide. For the previously defined functions of the goal of precision and reliability of the geodetic network, the optimal number and arrangement of GNSS network vectors was determined applaying first order design optimization. The optimization determined the design of the geodetic GNSS network, which contains 11 base vectors between 6 points of the network, the realization which requires 6 measurement sessions, which is 4 sessions shorter than the initial design.

An approach for using lasso (Least Absolute Shrinkage and Selection Operator) regression in creating sparse 3D models of soil properties for spatial prediction at multiple depths is presented. Modeling soil properties in 3D benefits from interactions of spatial predictors with soil depth and its polynomial expansion, which yields a large number of model variables (and corresponding model parameters). Lasso is able to perform variable selection, hence reducing the number of model parameters and making the model more easily interpretable. This also prevents overfitting, which makes the model more accurate. The presented approach was tested using four variable selection approaches – none, stepwise, lasso and hierarchical lasso, on four kinds of models – standard linear model, linear model with polynomial expansion of depth, linear model with interactions of covariates with depth and linear model with interactions of covariates with depth and its polynomial expansion. This framework was used to predict Soil Organic Carbon (SOC) in three contrasting study areas: Bor (Serbia), Edgeroi (Australia) and the Netherlands. Results show that lasso yields substantial improvements in accuracy over standard and stepwise regression — up to 50 % of total variance. It yields models which contain up to five times less nonzero parameters than the full models and that are usually more sparse than models obtained by stepwise regression, up to three times. Extension of the standard linear model by including interactions typically improves the accuracy of models produced by lasso, but is detrimental to standard and stepwise regression. Regarding computation time, it was demonstrated that lasso is several orders of magnitude more efficient than stepwise regression for models with tens or hundreds of variables (including interactions). Proper model evaluation is emphasized. Considering the fact that lasso requires meta-parameter tuning, standard cross-validation does not suffice for adequate model evaluation, hence a nested cross-validation was employed. The presented approach is implemented as publicly available sparsereg3D R package.

The latest applications of hedonic dwelling price models have included recent advances in spatial analysis that control for spatial dependence and heterogeneity. The study of spatial aspects of hedonic modelling pertains to spatial econometrics, which is relevant to this study because it clearly accounts for the influence and peculiarities related by space in real estate price modeling analysis.

Prevailing climatic conditions and local topography can be classified as the most influential environmental factors that affect the spatial dispersion of pollutants emanating from industrial sources. In this study, the combined effects of these factors were considered with respect to terrain exposure in order to explain the complex spatial trend of Arsenic (As) concentration that was atmospherically-deposited from one of the largest Copper Mining and Smelting Complexes in Europe, Bor in Serbia. Several exposure parameters were created and employed as spatial covariates within the so-called “Spline-Then-Krige” approach for producing maps of As concentration at three standard soil depth layers (0–5 cm, 5–15 cm and 15–30 cm). The exposure parameters were created to quantify two different aspects of terrain exposure: Geometrical (Proximity) and Topographical exposure. Regression analysis confirmed the presence of a significant statistical association between the As data and all exposure parameters. The trend model showed good overall accuracy explaining 52% of the variance in As data for the surface soil layer, 49% for the middle layer and 35% for the deepest layer. Relative importance analysis revealed the importance of considering a more general model that includes interactions between exposure parameters. The kriging interpolation improved, to some extent, the regression accuracy for all three layers with R2 values ranging from 55% for the surface layer to the 36% for the deepest soil layer. The prediction maps show that As contamination levels are well above allowable Serbian agricultural concentration limits (As < 25 mg/kg) for approximately 78% of the mapping area, thereby indicating that long term smelting activity leaves significant consequences on soil even on deeper unexposed layers. Journal of Geochemical Exploration (2017), 179: 25-35

Paper will present basic definitions and significance of implementation of information systems for facility maintenance, as well as terms Facility management (FM) and FM lifecycle. The main focus of this paper is on CAD and GIS integration within ArcGIS software in order to consolidate different data formats for creating unique information system for FM.

More widely used geostatistical methods for modeling distributed phenomena requires the evaluation of the quality of the product (maps) obtained by their application. The method of evaluation of uncertainty i.e. quality of the map was described on the example of a map of mean annual air temperature in Serbia for the period from the years 1991 to 2009 that was obtained from a relatively small number of samples for the whole country area (110 meteorological stations). The uncertainty of the map, obtained by kriging interpolation was evaluated by applying a Monte Carlo simulation modeling method. [Projekat Ministarstva nauke Republike Srbije, br. III47014, br. III43007 i br. TR 36009]

This paper presents the procedure of structure behaviour analysis and modelling applying the subspace method, which has been used in system identification but not in geodetic practice. This method enables the state space dynamic model parameters to be estimated. The equation error model has been used to form the dynamic system model. In addition, two pylons of the same bridge have been demonstrated to behave differently due to the position of the Sun. The entire system identification procedure has been demonstrated in modelling behaviour of the “Sloboda” bridge over the Danube pylon in direction of Novi Sad. The measurements were compiled during the bridge test load conducted in September 2005. The input signals subject to the analysis are the long-periodic signals: namely, the force in pylon cables and temperature. The output signals are pylon top displacements. The civil engineering experts' requirements called for identification of all displacements exceeding 10 mm. The pylon top position was determined by static GPS along with the 25- minute measurements per epoch.

The appearence of geostatistics and geographical information systems has made it possible to analyze complex spatial patterns of meteorological elements over large areas in the applied climatology. The objective of this study is to use geostatistics to characterize the spatial structure and map the spatial variation of average values of precipitation for a 30-year period in Serbia. New, recently introduced, geostatistical algorithms facilitate utilization of auxiliary variables especially remote sensing data or freely available global datasets. The data from Advanced Spaceborn Thermal Emission and Reflection Radiometer global digital elevation model are incorporated as ancillary variables into spatial prediction of average annual precipitation using geostatistical method known as regression kriging. The R 2 value of 0.842 proves high performance result of the prediction of the proposed method.