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Identification of movements using different geodetic methods of deformation analysis.

In the paper the description of an experiment for a comparative analysis of two different methods for deformation determination, geodetic and 2D deflection sensors based on fiber-optic curvature sensors (FOCSs) is given. The experiment is performed by a using specially designed assembly which makes it possible to apply both methods. For performing geodetic measurements, a geodetic micro-network is established. Measurements by applying a 2D deflection sensor and three total stations are carried out for comparison. The data processing comprises graphical and numerical analysis of the results. Based on the presented results the potential of 2D deflection sensor application in structural health monitoring (SHM) procedures is indicated. The analysis of the measurement results also indicates the importance of integrating various types of sensors for obtaining more accurate and more reliable deformation measurements results.

In this paper, an original modification of the generalised robust estimation of deformation from observation differences (GREDOD) method is presented with the application of two evolutionary optimisation algorithms, the genetic algorithm (GA) and generalised particle swarm optimisation (GPSO), in the procedure of robust estimation of the displacement vector. The iterative reweighted least-squares (IRLS) method is traditionally used to perform robust estimation of the displacement vector, i.e., to determine the optimal datum solution of the displacement vector. In order to overcome the main flaw of the IRLS method, namely, the inability to determine the global optimal datum solution of the displacement vector if displaced points appear in the set of datum network points, the application of the GA and GPSO algorithms, which are powerful global optimisation techniques, is proposed for the robust estimation of the displacement vector. A thorough and comprehensive experimental analysis of the proposed modification of the GREDOD method was conducted based on Monte Carlo simulations with the application of the mean success rate (MSR). A comparative analysis of the traditional approach using IRLS, the proposed modification based on the GA and GPSO algorithms and one recent modification of the iterative weighted similarity transformation (IWST) method based on evolutionary optimisation techniques is also presented. The obtained results confirmed the quality and practical usefulness of the presented modification of the GREDOD method, since it increased the overall efficiency by about 18% and can provide more reliable results for projects dealing with the deformation analysis of engineering facilities and parts of the Earth’s crust surface.

In this paper, a modified Generalised Robust Estimation of Deformation from Observation Differences (GREDOD) method is presented, based on the application of genetic algorithm (GA) and generalised particle swarm optimisation (GPSO) algorithm in solving the optimisation problem of this method, which is, in essence, a problem of determining the optimal datum of the displacement vector. The procedure of deformation analysis using this modification of the GREDOD method is demonstrated in the example of the two-dimensional geodetic network presented in numerous research and in which all observations and displacements were simulated. Using both algorithms, GA and GPSO, almost identical results of deformation analysis were obtained, except datum solutions of the displacement vector, which are completely different. These results differ only slightly from the results obtained using the methods of Hannover, Karlsruhe, Delft, Fredericton, München, Caspary, and the classical robust method.

3D visualization оf space and facilities providing realistic environmental representation. Modern technologies such as LIDAR gives opportunities of space inventory with great accuracy, and modern software solutions offer the possibility of visualizing and modeling the same. Density of terrain structural lines and ensuring accuracy level for all tipes of design, LIDAR technology in terms of efficiency beyond traditional techniques of mass data collection. The paper presents the final products of technology that represent the basis for the development for repairing projects, reconstruction and built design of engineering structures, such as bridges, tunnels, transport infrastructure, urban complexes, etc.

Traditionally, in order to solve geodetic network optimization problems analytical methods such as linear and nonlinear programming or simulation methods are used. However, global optimization techniques such as genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are increasingly used today to solve geodetic network optimization problems. In this paper, an application of these algorithms in second order design optimization of the geodetic network for the dam Liverovići is proposed. Since the subject of the analysis is the geodetic network for monitoring the dam, an optimality criterion is defined based on the sensitivity parameters of the geodetic network.

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