This article explores the use of machine learning methods in vibrodiagnostics. To assess the effectiveness of the use of a neural network, a hypothesis was put forward on the possibility of revealing hidden patterns indicating a defect for structures of the same type with different parameters. The selection of these features by visual analysis of the graphs is difficult due to the large amount of data, which indicates the relevance of solving the classification problem using a neural network. As a result of the study, a fully-connected two-layer neural network was obtained, showing sufficient accuracy of predictions, which confirms the fundamental possibility of using machine learning methods to monitor the state of standard structures.

Keywords: vibration diagnostics, building construction, building, structure, defect, damage, machine learning, neural network

The article presents a comparison of the main programs used in Russia to calculate the cost of construction. Statistics of the most popular estimated programs are given. A brief comparative analysis of the capabilities of the two programs, Grand Smeta and Smeta.ru, is presented. Based on the analysis, recommendations are given for choosing an effective settlement package for creating design estimates.

Keywords: estimate, construction, estimate programs, program complex, estimate calculations