딥러닝 알고리즘을 이용한 탄산화 진행 예측 정확성에 Hidden Layer 개수의 영향에 대한 실험적 연구
- Title
- 딥러닝 알고리즘을 이용한 탄산화 진행 예측 정확성에 Hidden Layer 개수의 영향에 대한 실험적 연구
- Other Titles
- An Experimental Study on the Effect of Number of Hidden Layers on Prediction Accuracy of Carbonation Process Using Deep Learning Algorithm
- Author
- 이한승
- Issue Date
- 2019-04
- Publisher
- 한국구조물진단유지관리공학회
- Citation
- 2019 한국구조물진단유지관리공학회 봄 학술발표회 논문집, Page. 315-316
- Abstract
- Carbonation of reinforced concrete is a major factor in the deterioration of reinforced concrete, and prediction of the resistance to carbonation is important in determining the durability life of reinforced concrete structures. In this study, basic research on the prediction of carbonation penetration depth of concrete using Deep Learning algorithm among artificial neural network theory was carried out. The data used in the experiment were analyzed by deep running algorithm by setting W/B, cement and blast furnace slag, fly ash content, relative humidity of the carbonated laboratory, temperature, CO2 concentration, Deep learning algorithms were used to study 60,000 times, and the analysis of the number of hidden layers was compared.
- URI
- http://db.koreascholar.com/Article?code=367779https://repository.hanyang.ac.kr/handle/20.500.11754/121819
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ARCHITECTURE(건축학부) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML