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The study investigates issues related to modeling and development of intelligent systems for estimation of the technical state of building constructions. The study considers mathematical bases of modeling of an estimation system based on a fuzzy knowledge base and one of modifications of Takagi-Sugeno-Kang fuzzy network. It describes the structure of a network in detail and substantiates a choice of the algorithm for its learning. The main criteria for choosing this modification were its ability to solve a classification problem under conditions of uncertainty and the ability to set rules by the function of inputs. We adapted the structure of a network to the task of estimation of the technical state of real building constructions. We showed that it is advisable to learn a network with a use if an algorithm with a trainer. In this case, we suggested to use a direct method of random search, which is adapted to the solution of this problem, to minimize an error. In order to identify the state of structures, we suggested to use membership measures obtained by the clustering method. Implementation and introduction of neural network technologies in solution of tasks of estimation of the technical state of building constructions expands and improves capabilities of intelligent systems, reduces risks of making incorrect decisions by increasing reliability and speed of modeling.

Modeling an intelligent system for the estimation of technical state of construction structures