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.

Building façade assessment could be performed in a more efficient way using a multidisciplinary approach and modern technologies. This study proposes the orthofaçade-based assisted inspection method (AIM), universal and applicable to different types of façade cladding and suitable for application in the condition assessment of inaccessible building façades or high-rise and large structures of all kinds. The AIM method offers a multidisciplinary approach by combining unmanned aerial vehicle (UAV) technology, electronic tachymetry, and digital image processing techniques (photogrammetry and open-source computer vision methods). The method was verified in a case study performed on a high-rise building façade. On-site data acquisition of high-resolution images of façade and control points was conducted by UAV and tachymetry. The data were further processed in photogrammetric software in order to generate a georeferenced orthofaçade. Crack detection was performed at pixel level via computer code using the OpenCV library methods. The established diagnostic model, defined by control points, enables precise determination of crack location. Crack length, width, or area could be calculated based on the coordinates of its points, by performing simple mathematical operations. The AIM method provides automation of crack detection and precise determination of location and geometrical parameters of detected crack.