Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

딥러닝 알고리즘 기반 탄산화 진행 예측에서 활성화 함수 적용에 관한 기초적 연구A Fundamental Study on the Effect of Activation Function in Predicting Carbonation Progress Using Deep Learning Algorithm

Other Titles
A Fundamental Study on the Effect of Activation Function in Predicting Carbonation Progress Using Deep Learning Algorithm
Authors
정도현이한승
Issue Date
Nov-2019
Publisher
한국건축시공학회
Keywords
concrete carbonation; deep learning; mixing factor; 콘크리트 탄산화, 딥러닝, 배합인자
Citation
한국건축시공학회 학술발표대회 논문집, v.19, no.2, pp 60 - 61
Pages
2
Indexed
OTHER
Journal Title
한국건축시공학회 학술발표대회 논문집
Volume
19
Number
2
Start Page
60
End Page
61
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/2253
Abstract
Concrete carbonation is one of the factors that reduce the durability of concrete. In modern times, due to industrialization, the carbon dioxide concentration in the atmosphere is increasing, and the impact of carbonation is increasing. So, it is important to understand the carbonation resistance according to the concrete compounding to secure the concrete durability life. In this study, we want to predict the concrete carbonation velocity coefficient, which is an indicator of the carbonation resistance of concrete, through the deep learning algorithm, and to find the activation function suitable for the prediction of carbonation rate coefficient as a process to determine the learning accuracy through the deep learning algorithm. In the scope of this study, using the ReLU function showed better accuracy than using other activation functions.
Files in This Item
Go to Link
Appears in
Collections
COLLEGE OF ENGINEERING SCIENCES > MAJOR IN ARCHITECTURAL ENGINEERING > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Lee, Han Seung photo

Lee, Han Seung
ERICA 공학대학 (MAJOR IN ARCHITECTURAL ENGINEERING)
Read more

Altmetrics

Total Views & Downloads

BROWSE