Models and methods of artificial intelligence in the process of performing building-technical expertise

The object of this research is the process of forming a conclusion building-technical expertise by a support system for the restoration of real estate objects. The subject of the study includes artificial intelligence models and methods capable of solving the task of forming an expert conclusion regarding the technical condition category of building structures and objects as a whole. The aim of this work is to justify the choice of a model for assessing the technical condition of real estate objects in building-technical expertise based on an analysis of artificial intelligence models and methods that can handle fuzzy classification tasks. To evaluate the technical condition of building structures and objects, the use of gradient-boosted decision trees is proposed. This method corrects errors from previous iterations and considers the magnitude of different types of errors. It has been demonstrated that the iterative learning mechanism allows experts in building-technical expertise to refine or supplement the data on which conclusions are based. Adjustments to the conclusions of gradient-boosted decision tree ensembles can be made by experts in accordance with the regulatory framework. The input and output data of the model have been formalized, taking into account such an anthropogenic factor as the impact of weaponry. Five main structural elements have been identified, for each of which it is advisable to train decision tree ensembles. A loss function has been introduced that allows special attention to be given to the critical states of buildings and structures, where the risk of error may lead to total unsuitability or functional failure of structures or their elements. Based on the analysis of a series of studies, the choice of multi-agent theory has been justified as a subject for further research to ensure the scalability and flexibility of the support system for the restoration of real estate objects.