Model of predictive calculation of technical and economic indicators of the prefabricated module

Introduction. Prefabrication and modular construction, as tools for increasing the pace of construction production, are becoming more and more in demand for the construction industry in the current economic environment due to the efficiency of installation and the ability to shorten project deadlines. However, despite these advantages, the use of information modelling technologies for automating the design processes of modular facilities and calculating technical and economic indicators remains at an insufficient level, since the potential of TIM in this area has not yet been fully realized. Therefore, it is advisable and necessary to automate the processes of various stages of the life cycle, including the calculation of project indicators for factory production. It was concluded that it is advisable to develop a predictive model that reduces the design time and increases the economic feasibility of prefabrication.

Materials and methods. The practice of automation of design processes based on BIM, in particular in the framework of factory-based production of modular structures is considered. The purpose of this study is to increase the efficiency of solving the life cycle management tasks of modular objects through the use of a predictive model using the statistical calculation of the TEI project for more rational data management at various design stages. To achieve this goal, a systematic review of approaches and tools for automating modelling and information processing processes was conducted.

Results. As a result of the analysis, a model was developed, the results obtained were analyzed, and the possibilities and advantages of applying this model within the framework of prefabrication were presented. Ways of further research within the framework of modular object design methodology were formulated.

Conclusions. The proposed data model can systematize and predict the TEI of prefabricated modules with sufficient accuracy based on IM and integration of statistical calculations, which improves decision making in the early stages of design, and helps to reduce the risk of errors and reduce the cost of redesign.

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