지원사업
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
- 2024.5
- 수록면
- 3 - 9 (7page)
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이 논문의 연구 히스토리 (3)
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초록· 키워드
오류제보하기
Accurately estimating a building’s lifespan is crucial for assessing its asset value and determining its economic and environmental feasibility, which is key for decision-making in the construction industry. However, because it’s nearly impossible to precisely estimate the lifespan of each building due to the wide range of influencing factors, most studies have used uniform lifespans based on the building’s primary structural type. To address this limitation, 1,812,700 records were analyzed of buildings constructed and demolished in Korea to predict each building’s lifespan with greater accuracy. Based on the previous study, a prediction model was developed using both deep learning and traditional machine learning methods. This study evaluated whether the building lifespan prediction model experienced overfitting based on the data period used to create the model. A performance evaluation was also conducted, comparing models using only key factors to those using a broader set of factors. The results showed that among the machine learning models, the artificial neural network model, a nonlinear approach, maintained high predictive accuracy without overfitting, regardless of the data period used. The model that used all available factors performed better than those based on just a few key factors. This research demonstrates the viability of using big data and AI for building lifespan prediction, providing a more reliable method for estimating building lifespan tailored to each building’s unique characteristics. This approach meets a growing societal demand for more accurate building lifespan predictions.
목차
Abstract
1. 서론
2. 문헌고찰
3. 연구방법
4. 결과
5. 결론
REFERENCES
참고문헌 (18)
참고문헌 신청
Čeh, M., Kilibarda, M., Lisec, A., & Bajat, B. (2018). Estimating the performance of random forest versus multiple regression for predicting prices of the apartments. ISPRS international journal of geo-information, 7(5), 168.
Chen, C. J., Juan, Y. K., & Hsu, Y. H. (2017). Developing a systematic approach to evaluate and predict building service life. Journal of Civil Engineering and Management, 23(7), 890-901.
El Naqa, I., Li, R., & Murphy, M. J. (2015). What is machine learning?. In machine learning in radiation oncology. Springer, Cham. 3.
Faisal, H. M., Javaid, N., Sarfraz, B., Baqi, A., Bilal, M., Haider, I., & Shuja, S. M. (2019, March 27-29). Prediction of building energy consumption using enhance convolutional neural network [Paper presentation]. WAINA 2019: Web, Artificial Intelligence and Network Applications, Matsue, Japan. https://doi.org/10.1007/978-3-030-15035-8_111
Ibánez, A. J. P., Bernal, J. M. M., de Diego, M. J. C., & Sánchez, F. J. A. (2016). Expert system for predicting buildings service life under ISO 31000 standard. Application in architectural heritage. Journal of Cultural Heritage, 18, 209-218.
Chen, C. J., Juan, Y. K., & Hsu, Y. H. (2017). Developing a systematic approach to evaluate and predict building service life. Journal of Civil Engineering and Management, 23(7), 890-901.
El Naqa, I., Li, R., & Murphy, M. J. (2015). What is machine learning?. In machine learning in radiation oncology. Springer, Cham. 3.
Faisal, H. M., Javaid, N., Sarfraz, B., Baqi, A., Bilal, M., Haider, I., & Shuja, S. M. (2019, March 27-29). Prediction of building energy consumption using enhance convolutional neural network [Paper presentation]. WAINA 2019: Web, Artificial Intelligence and Network Applications, Matsue, Japan. https://doi.org/10.1007/978-3-030-15035-8_111
Ibánez, A. J. P., Bernal, J. M. M., de Diego, M. J. C., & Sánchez, F. J. A. (2016). Expert system for predicting buildings service life under ISO 31000 standard. Application in architectural heritage. Journal of Cultural Heritage, 18, 209-218.
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