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
- 저널정보
- 한국계산역학회 Computers and Concrete, An International Journal Computers and Concrete, An International Journal Vol.24 No.6
- 발행연도
- 2019.1
- 수록면
- 555 - 560 (6page)
이용수
초록· 키워드
오류제보하기
During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.
목차
등록된 정보가 없습니다.
참고문헌 (40)
참고문헌 신청
Acharya, U. R. / 2017 / Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals / Inform. Sci 415(1):190~198
Alshehhi, R. / 2017 / Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / ISPRS J. Photogram. Remote Sens 130:139~149
Armengaud, J. / 2017 / Characterization of fresh dry-mix shotcrete and correlation to rebound / Constr. Build. Mater 135:225~232
Ballou, M. / 2003 / Shotcrete Rebound-How Much Is Enough? / American Concrete Association, Shotcrete Magazine Fall, Spring
Baricevic, A. / 2018 / Effect of polymer fibers recycled from waste tires on properties of wet-sprayed concrete / Constr. Build. Mater 176:135~144
Alshehhi, R. / 2017 / Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / ISPRS J. Photogram. Remote Sens 130:139~149
Armengaud, J. / 2017 / Characterization of fresh dry-mix shotcrete and correlation to rebound / Constr. Build. Mater 135:225~232
Ballou, M. / 2003 / Shotcrete Rebound-How Much Is Enough? / American Concrete Association, Shotcrete Magazine Fall, Spring
Baricevic, A. / 2018 / Effect of polymer fibers recycled from waste tires on properties of wet-sprayed concrete / Constr. Build. Mater 176:135~144
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