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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.2
- 수록면
- 267 - 273 (7page)
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초록· 키워드
오류제보하기
In construction projects, the timeframe often relies on the project manager"s experience or past construction records rather than a quantitative workload analysis. Accurate predictions necessitate estimating based on actual construction duration data, factoring in the workload. However, integrating construction duration predictions into machine learning models requires extensive big data, and missing data is a common challenge. This study aims to enhance the learning performance of construction duration prediction models by employing and comparing various imputation methods in the data preprocessing stage. Suitable imputation methods were proposed for machine learning model training based on the average error rate. Results showed that the median imputation method was the most fitting single imputation method, while the random forest regression imputation method stood out among multiple imputation methods. Additionally, with an increasing volume of data, regression imputation methods within multiple imputation proved more suitable than single imputation methods.
목차
Abstract
1. 서론
2. 문헌고찰
3. 결측치 대체방안 제시를 위한 공기 예측모델 구성
4. 결론
REFERENCES
참고문헌 (11)
참고문헌 신청
Cho, B. N., Kim, H. S., & Kang, L. S. (2015). Development of a Neural Network-Based Model for Estimating Working Periods for Each Construction Process, Journal of the Korea Academia-Industrial Cooperation Society, 16(5), 3477-3483.
Cheng, M. Y., & Hoang, N. D. (2018). Estimating construction duration of diaphragm wall using firefly-tuned least squares support vector machine. Neural Computing and Applications, 30, 2489-2497.
Hwang, H. S., Kim, K. R., Suh, S. W., Kim, C. D., & Shin, D. W. (2002). Comparative Analysis of Actual Air Conditioning by Factors Influencing Air Calculation - Focused on the Office Building Construction Air Conditioning Standards, Construction Management: Journal of the Korean Institute of Construction Management, 3(3), 84.
Kim, J. S., & Kim, Y. S. (2021). Development of Construction Duration Estimation Model for Urban-type Residential Housing based on Multiple Regression Analysis. LHI Journal of Land, Housing, and Urban Affairs, 12(4), 93-101.
Kim, S. W. (2021). A Study on the Prediction of Skeleton Construction Period using DNN. Master's Thesis, Hanyang University, 34.
Cheng, M. Y., & Hoang, N. D. (2018). Estimating construction duration of diaphragm wall using firefly-tuned least squares support vector machine. Neural Computing and Applications, 30, 2489-2497.
Hwang, H. S., Kim, K. R., Suh, S. W., Kim, C. D., & Shin, D. W. (2002). Comparative Analysis of Actual Air Conditioning by Factors Influencing Air Calculation - Focused on the Office Building Construction Air Conditioning Standards, Construction Management: Journal of the Korean Institute of Construction Management, 3(3), 84.
Kim, J. S., & Kim, Y. S. (2021). Development of Construction Duration Estimation Model for Urban-type Residential Housing based on Multiple Regression Analysis. LHI Journal of Land, Housing, and Urban Affairs, 12(4), 93-101.
Kim, S. W. (2021). A Study on the Prediction of Skeleton Construction Period using DNN. Master's Thesis, Hanyang University, 34.
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