Predicting the deterioration of building structures using a logistic model for repair and maintenance works

The aim of the study is to develop methods for assessing the technical condition of building elements to optimize the planning and execution of repair and restoration works during the operational phase. Current research in the field of building condition monitoring traditionally focuses on the physical characteristics of materials and structures, which limits its practical application for large-scale repair planning. In contrast, facility management requires methodologies based on accessible data (visual inspections, repair history) to optimize budget planning within capital repair programs. The proposed phase analysis method, based on a logistic wear model and utilizing repair cost dynamics, addresses this challenge by combining objective technical assessment with the practical needs of facility management organizations. The algorithm for identifying transitions between degradation phases using objective criteria includes: calculating degradation process rate characteristic; determining inflection points on wear curves; estimating residual service life for different types of structures. By analyzing annual cost growth coefficients kt, their geometric mean k¯, and relative deviations Δkt), three characteristic degradation phases are established: initial, accelerated, and critical. This approach enables early detection of elements with nonlinear restoration cost growth. The model has been tested on various building elements, and recommendations are provided for optimizing repair strategies based on the phase states of the logistic wear curve for building structures.