Development paths of ontology-driven support systems for building-technical expertise

Abstract

The object of the research is the process of assessing the technical condition of objects within the scope of building-technical expertise. The subject of the study is the ontological approach to knowledge formalization and machine learning methods capable of solving the problem of evaluating the technical condition of damaged or destroyed structures. It is demonstrated that the increasing scale of physical destruction, the absence of a structured mechanism for processing expert information, and the need for coordination among different institutions highlight the necessity of developing a formalized and normatively consistent decision support system for experts. The paper explores the applicability of the ontological approach to the formal representation of knowledge in the field of construction and technical assessment. The structure of the ontology is outlined, along with its components and the stages of its development. A taxonomic model has been constructed, defining classes of structures, structural elements, types of damage, sets of influencing factors, and sources of the regulatory framework. The integration of machine learning methods as a tool for inductive data generalization within the ontological framework is substantiated. Examples of relevant algorithms are provided, including gradient boosting of decision trees, random forest, support vector machines, and the k-nearest neighbors method. It is emphasized that the system must ensure transparency, explainability, and compliance with regulatory requirements, while leaving the final decision to the domain expert. The results of this study can be used to develop a decision support system for assessing the technical condition of structures and formulating recommendations for their restoration.