[논문]인공신경망 기반 동결융해 작용을 받는 콘크리트의 내구성능 평가

할리오나; 허인욱; 최승호; 김강수

doi:10.11112/jksmi.2023.27.6.144

인공신경망 기반 동결융해 작용을 받는 콘크리트의 내구성능 평가
Estimation of Concrete Durability Subjected to Freeze-Thaw Based on Artificial Neural Network 원문보기

한국구조물진단유지관리공학회 논문집 = Journal of the Korea Institute for Structural Maintenance and Inspection, v.27 no.6, 2023년, pp.144 - 151

할리오나 (서울시립대학교 건축공학과 스마트시티융합전공) , 허인욱 (서울시립대학교 도시방재안전연구소) , 최승호 (서울시립대학교 방재공학과) , 김강수 (서울시립대학교 건축공학과 스마트시티융합전공)

초록
AI-Helper

이 연구에서는 동결융해 작용을 받는 다양한 콘크리트 배합에 대한 실험결과를 수집하여 데이터베이스를 구축하였다. 이를 바탕으로 동결융해 작용을 받는 콘크리트의 인공지능 기반 내구성능 평가모델을 개발하였으며, 회귀분석을 통해 상대동탄성계수 추정식을 도출하였다. 제안된 인공신경망 모델의 오류율과 결정계수는 각각 약 10.4%와 0.7이었으며, 회귀분석 추정식도 유사한 결과를 나타내었다. 따라서, 제안된 인공신경망 모델 및 회귀분석 추정식은 다양한 배합의 동결융해 작용을 받는 콘크리트에 대한 상대동탄성계수를 추정하는 데에 활용될 수 있을 것으로 판단된다.

Abstract ▼ AI-Helper

In this study, a database was established by collecting experimental results on various concrete mixtures subjected to freeze-thaw cycles, based on which an artificial neural network-based prediction model was developed to estimate durability resistance of concrete. A regression analysis was also conducted to derive an equation for estimating relative dynamic modulus of elasticity subjected to freeze-thaw loads. The error rate and coefficient of determination of the proposed artificial neural network model were approximately 11% and 0.72, respectively, and the regression equation also provided very similar accuracy. Thus, it is considered that the proposed artificial neural network model and regression equation can be used for estimating relative dynamic modulus of elasticity for various concrete mixtures subjected to freeze-thaw loads.

주제어

표/그림 (15)

그림 Fig. 1 Types of freeze-thaw damage
표 Table 1 Information of training database from literature
표 Table 2 Database of freeze-thaw resistance
그림 Fig. 2 Freeze-thaw resistance prediction model
그림 Fig. 3 ANN structure
표 Table 3 Range of input parameters and RDME
그림 Fig. 4 ANN training results
표 Table 4 Weight and bias
표 Table 5 Training results of ANN model
그림 Fig. 5 ANN validation results
그림 Fig. 6 Importance evaluation results
표 Table 6 Validation results of ANN model
그림 Fig. 7 Correlation for each variables with RDME
표 Table 7 Comparison between equation with experiment
그림 Fig. 8 Results of Resistance evaluation equation

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