Methods for monitoring high-rise buildings with allowance for variable stiffness characteristics of structures

Purpose. The purpose of the study is to develop an applied approach to integrated monitoring of high-rise buildings for the quantitative assessment of changes in the stiffness of the structural system and for supporting engineering decisions under routine and post-incident operating conditions. Methodology. The study is based on a comparative analysis of GNSS methods, accelerometric and inclinometric monitoring, terrestrial laser scanning, computer vision, SHM and PS-InSAR, as well as on kinematic, modal and stiffness indicators. For consistent interpretation of the results, the sequence "measurement – calculation – interpretation – decision" and an integrated SCI indicator are proposed; this indicator combines global displacements, changes in natural frequencies and the maximum inter-storey drift. Findings. It is shown that the combined analysis of displacements, modal characteristics, inter-storey shifts and equivalent global stiffness provides a more stable diagnostic picture than single-channel monitoring schemes. A demonstration example of a 23-storey building presents the sequence for assessing stiffness changes and interpreting the obtained indicators. Originality. A complete engineering and methodological procedure is proposed for combining kinematic, modal and geometric features within an integrated SHM framework, taking into account the conditions of application in Ukraine. Practical value. The results may be used for preliminary selection of the monitoring-system configuration, prioritisation of instrumental inspections and justification of the subsequent operating mode of high-rise buildings under conditions of restricted access to facilities and increased operational risks. The proposed structure is considered as a basis for further refinement of threshold values, weighting coefficients and sensor composition, depending on the structural scheme of the building and the availability of instrumental data.