High industrial chimney inclination monitoring and analysis is crucial from a stability point of view because, if not maintained, it can pose a great potential hazard for its surroundings. Various modern approaches of chimneys' geometrical parameters determination have been proposed and are actively in use. However, little research regarding the applicability of the unmanned aerial system (UAS)-based photogrammetric approach of chimney structural monitoring has been conducted and a comprehensive analysis with validated methods is lacking. Therefore, this research is focused on the determination of geometrical structural parameters of a masonry chimney whereby two independent methods have been applied. Reference values of the chimney geometrical parameters have been determined by precise total station (TS) measurements and, in relation to them, the applicability of the UAS-based photogrammetric approach is evaluated. Methodologically, the reference and validation values of the chimney geometrical parameters have been determined based on double modeling of the chimney structure. Firstly, cross-sectional elliptical regression has been applied to determine the geometrical values of the chimney at predefined above-ground levels (AGLs). Secondly, the spatial chimney axis has been derived by polynomial regression to determine the inclination of the full chimney structure. Lastly, the structural stability of the chimney is validated based on its axis inclination whereby permitted deviations are determined according to the European Standard EN 1996-1-1:2005. Experimental results of our research show that consistently better results are gained by TS-based surveys and, although the determination of the chimney's geometrical values by the UAS-based approach is certainly possible, great attention must be given to the accuracy of the UAS-generated point cloud (PC) if high accuracy results are needed.
Keywords: axis inclination; elliptical regression; geodetic reference network (GRN); masonry chimney; photogrammetry; point cloud (PC); polynomial regression; total station (TS); unmanned aerial system (UAS).