The paper analyses the possibility of increasing efficiency of the Iterative Weighted Similarity Transformation (IWST) method, which is a prototype of classic robust methods, using global optimisation approach instead of classical one, available in the literature. For the purpose of solving the optimisation problem of the IWST method, in addition to the Iterative Reweighted Least Squares (IRLS) method, the Genetic algorithm (GA) and Generalised Particle Swarm Optimisation (GPSO) algorithm were applied, in order to overcome some flaws of IRLS method. Experimental research was performed based on the Monte Carlo simulation using the mean success rate (MSR) on the example of the geodetic control network for monitoring the Šelevrenac dam in the Republic of Serbia. By using the GA and GPSO algorithms, the overall efficiency of the IWST method has been increased by about 18% compared to the IRLS method. Also, it has been determined that the efficiency of the IRLS method significantly reduces with the increase in the number of displaced potential reference points (PRPs), while the GA and GPSO algorithms’ efficiency does not change significantly. The values of overall absolute true errors due to the increased number of displaced PRPs in the GA and GPSO algorithms did not change notably while with the IRLS method their values increased significantly.

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.

The aim of this article is to analyse and estimate the systematic errors of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), assuming the existence of systematic influences due to the erroneously determined altitude datum. The assumption is based on the identical sign for the majority of differences between the control point elevations and the elevations derived from GDEM. The question is how to model a systematic influence and whether such a model can improve the global GDEM accuracy. The error analysis comprises two unbiased test areas (flat and hilly areas), where one uses the elevations of the control points as conditionally correct values. Three methods of estimating and eliminating the systematic errors are presented: a constant-systematic-error model, a variable-systematic-error model by first-order polynomial and a variable-systematic error model by second-order polynomial. The accuracy analysis concerning the original and calibrated GDEMs indicates a significant accuracy improvement in the case of the calibrated methods for both test areas, especially for the flat area.

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.