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In the era of Industry 4.0, technological advancements are transforming the construction industry through automation, artificial intelligence (AI), and data-driven decision-making. Traditional structural design methods, particularly for reinforced concrete beams and columns based on the Vietnamese Standard TCVN 5574:2018, involve multiple manual calculations that, while effective, are time-consuming and labor-intensive. To address this limitation, this study proposes a Supervised Machine Learning (SML) approach to optimize reinforcement design for beams and columns. Using available datasets, the SML models can predict the required reinforcement area with high accuracy, achieving deviations of less than 10% for beams and 13% for columns. The application of SML in reinforcement estimation significantly reduces the time required for structural calculations. Moreover, it lays the foundation for future developments in automated structural design processes through seamless integration with architectural and structural design software and programming environments such as REVIT, ETABS/SAP2000, and MATLAB.

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Optimized Supervised Machine Learning for Accurate Estimation of Reinforcement in RC Beams and Columns

  • 会议论文

  • 发表日期：

    2026年

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  • Nhan Thanh Vu Nguyen,
  • Chon Tran,
  • Duong Thai Le,
  • Quy Thue Nguyen

摘要

In the era of Industry 4.0, technological advancements are transforming the construction industry through automation, artificial intelligence (AI), and data-driven decision-making. Traditional structural design methods, particularly for reinforced concrete beams and columns based on the Vietnamese Standard TCVN 5574:2018, involve multiple manual calculations that, while effective, are time-consuming and labor-intensive. To address this limitation, this study proposes a Supervised Machine Learning (SML) approach to optimize reinforcement design for beams and columns. Using available datasets, the SML models can predict the required reinforcement area with high accuracy, achieving deviations of less than 10% for beams and 13% for columns. The application of SML in reinforcement estimation significantly reduces the time required for structural calculations. Moreover, it lays the foundation for future developments in automated structural design processes through seamless integration with architectural and structural design software and programming environments such as REVIT, ETABS/SAP2000, and MATLAB.