Earticle

초록

영어

In this study, an artificial neural networks study was carried out to predict the quantity of ammonia gas (NH3) of Granulated Blast Furnace Slag (GBFS) cement mortar. A data set of a laboratory work, in which a total of 4 mortars were produced, was utilized in the Artificial Neural Networks (ANNs) study. The mortar mixture parameters were four different GBFS ratios (0%, 20%, 40% and 60%). Measurement ammonia of moist cured specimens were measured at 1, 3, 10, 30, 100, 365 days. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of two input parameters that cover the cement, GBFS and age of samples and, an output parameter which is concentrations of ammonia emission of mortar. The results showed that ANN can be an alternative approach for the predicting the ammonia concentration of GBFS mortar using mortar ingredients as input parameters.

목차

Abstract
 1. Introduction
 2. Experiments
  2.1. Properties of Materials
  2.2. Mortar Mixture Proportions
  2.3. Test Procedure
  2.4. Theory and Calculation
  2.5. Methods
 3. Results and Discussion
  3.1. Experimental Results
  3.2. Artificial Neural Network Model for Prediction of Experimental Results
 4. Conclusion
 Acknowledgement
 Reference

키워드

저자

• Hongseok Jang [ Dept. of Architectural Engineering, Chonbuk National University, Republic of Korea ]
• Malrey Lee [ The research Center for Advanced Image and Information Technology, School of Electronics and information Engineering, Chonbuk National University, Republic of Korea ]
• Seungyoung So [ Research Center of Industrial Technology, Dept. of Architectural Engineering, Chonbuk National University, Republic of Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605