Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
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Analysis and Prediction of Quantity Takeoff in Reinforced Concrete Apartments Using Open-source Machine Learning Techniques

오픈소스 머신러닝을 이용한 철근콘크리트 아파트의 물량 분석 및 예측

  • Lee, Jae-Cheol (Dept. of Architectural Engineering, Tongmyong University)
  • Received : 2024.03.19
  • Accepted : 2024.05.16
  • Published : 2024.05.30
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Abstract

Accurate analysis and prediction of Quantity Takeoff (QTO) is crucial for construction project success, enabling efficient resource management, cost and time savings, quality improvement, and project continuity. However, current QTO methods rely on manual labor, posing challenges in productivity and accuracy due to the dependence on workers' experience and skills. This study utilizes an open-source machine learning-based data analysis tool to analyze major QTO components such as reinforcement, concrete, and formwork in a previously executed apartment project. By integrating fundamental project information applicable in the early stages, a predictive model capable of estimating major quantities based on various situational variable combinations was proposed and its reliability was validated.

Keywords

Acknowledgement

이 논문은 2023학년도 동명대학교 연구년지원에 의하여 연구되었음.

References

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