Integration of artificial intelligence technologies with BIM platforms for predictive quality control of high-rise structures: comparative analysis of machine learning algorithms
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Abstract
The rapid development of digital technologies in the construction industry creates new opportunities for improving the quality of construction processes through the integration of artificial intelligence with building information modeling. This study is devoted to the development of a methodology for integrating machine learning algorithms with BIM platforms for predictive quality control in high-rise construction. The study is based on a comparative analysis of the effectiveness of various machine learning algorithms in relation to the tasks of predicting defects in building structures. As part of an empirical study, data from 284 high-rise construction projects implemented in the period 2020-2024 in various climatic zones were analyzed. The results showed that the integrated AI-BIM system provides an increase in the accuracy of forecasting defects in reinforced concrete structures to 91.3% when using convolutional neural networks, compared with 67.4% for traditional control methods. Random forest algorithms have demonstrated 88.7% efficiency in predicting the strength characteristics of concrete with a coefficient of determination R2 = 0.887. The economic effect of implementing the system is to reduce the cost of eliminating defects by 24.3% and reduce the time for detecting inconsistencies by 42.1%. The proposed methodology facilitates the transition from reactive to predictive quality management in construction and opens up prospects for the further development of intelligent construction technologies.
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References
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