Abstract. The article considers the process of development of an information model containing both graphic and non-graphic information in a common data environment. It also gives some information concerning this process. On the basis of this information a conclusion is being drawn that the connection of additional data with the existing information models may produce a deep comprehension of a construction project, that is a design cost estimate.In its term, this property isn't expressed by a combination of others (questions of the order of erection works, various performance characteristics, a necessary project support), and it may be considered as a linear-independent vector. The authors also developed and presented an algorithm of formation of a collection of consolidated factors of the building construction cost. On the basis of the aforesaid algorithm, the conclusion is drawn that the introduction of data for the evaluation of expenditures into the information model is more efficient with the use of the existing normative basis. This fact allows the authors to show how to calculate the project cost at the stage of its development using the existing construction cost norms. The calculation of the project cost at this stage may contribute to the process of decision making, and it leads, finally, to the development of an efficient design model using an information model.
This paper focuses on the problem of automatic defect detection in building materials and the use of deep learning and pattern recognition to solve this problem. The paper describes various methods that can be used to solve this problem, including transfer learning, data augmentation, and fine-tuning, and discusses the advantages and limitations of each approach. The article also describes a convolutional neural network (CNN) architecture that can be used to detect defects in building materials, specifying the purpose and functionality of each layer. In addition, the article presents the mathematical formulas necessary for this approach, including the convolution operation, the ReLU activation function, the maximum association operation, the dropout operation, and the sigmoid activation function. Overall, the paper highlights the potential of deep learning and pattern recognition in building materials quality control and the benefits that automated systems can bring to the construction industry. The use of these technologies can increase efficiency, reduce costs, and improve the quality of construction projects, ultimately leading to safer and more durable structures.
This paper discusses the main types of neural networks and describes which are most suitable for solving problems within the framework of natural language user interfaces. The statistics of publications on the use of neural networks in various branches of science for the period from 2015 to 2020 are given, assumptions about the possible directions of integration of neural networks in the construction industry are made.
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