Статья представляет собой исследование, направленное на анализ текущего состояния качества и количества машиномест, определение требований для решения оптимизационных задач, касающихся планировок общественных объектов, степени проработанности темы. Анализ выполнен на основе поиска литературных источников. Отдельное внимание уделяется нормативно-правовым актам, содержащим требования к объемно-планировочным решениям. В результате работы можно заключить, что большинство исследований носят общий характер и не рассматривают конкретные примеры применения технологии для оптимизации планировочных решений парковочных пространств. Вышеупомянутое позволяет заключить о перспективности направления и актуальности поставленной проблемы.
Introduction. The regulatory framework of building information modelling is in the process of proactive development. The development of a construction information classifier is an important step towards effective transition to digital construction. The classifier can serve as the basis for a large number of scenarios, starting from the simplest model navigation and ending with various practically valuable results obtained in the form of project budgets, statements of work amounts, and materials. In practice, classification takes a long time and requires new approaches to process automation. An innovative solution to this problem is artificial intelligence algorithms, which are a forecasting tool employing an automatic method used to enter code into an information model using processed source data and pre-trained AI models. Materials and methods. The material to be studied is the data prepared for a training set based on digital information models of civil and industrial facilities. Results. Russian and foreign classifiers of construction information were studied; machine learning models were considered; a training set was made and processed using digital information models of civil and industrial facilities, and classification models were evaluated using the processed data. The highest quality classification model was selected using the criteria of preprocessing velocity, training/retraining time and the F1 score. Conclusions. A random forest machine learning model can be used as the main artificial intelligence algorithm to classify construction information. This solution will accelerate the classification process due to the automatic code entry into the model and increase the efficiency of work processes.
Развитие компьютерных технологий оказало большое влияние на строительную отрасль. В современных реалиях участникам строительного процесса приходится обрабатывать большие объемы информации, вследствие чего появилась потребность в автоматизации строительных процессов. В статье была рассмотрена и проанализирована проблема расчета объемов отделки помещений. На основе готовой архитектурной модели был проведен анализ всех существующих методов расчета, а также выявлены их сильные и слабые стороны. По полученным выводам проведенного анализа был создан новый алгоритм расчета отделки, реализованный с помощью визуального программирования. Были обоснованы преимущества использования предложенного способа.
The article describes how to transform the time-consuming process of selecting design solutions for parking spaces according to regulatory requirements and terms of reference by building information modelling (BIM). The main purpose of the work is to determine the possibility of applying a generative approach to the parking lot design by creating an optimization model of variant design. The process of selecting design solutions for parking is considered by using mathematical modeling and multi-criteria optimization. A mathematical description of finding the optimal solution on the region of admissible ratios is obtained both between the two types of parking spaces required by regulatory documents (type 1-Standard, with dimensions of 5.3 × 2.5 meters; type 2 - for People with Limited Mobility (PLM), with dimensions of 6.0 × 3.6 meters) and between the occupied area and the total number of parking spaces. The optimization model of choosing design solutions for parking lot helps a design engineer to adapt changes in requirements for defining admissible options for space-planning solutions. The study proves that the task of choosing admissible and optimal solutions for the parking lot, depending on the set of conditions, can be performed by using algorithms, and, consequently, by using computer-aided design in BIM. The proposed approach to parking lot design allows the project organization to coordinate the initial conditions and solutions between the project participants and related departments, and also serves as the basis for solving subsequent design tasks, such as determining the economic efficiency, the safety of the parking project, and others.
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