한국산학기술학회논문지 (Journal of the Korea Academia-Industrial cooperation Society)

한국산학기술학회 (The Korea Academia-Industrial cooperation Society)
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외부영향요인을 고려한 콘크리트 강도예측 뉴럴 네트워크 모델

Concrete Strength Prediction Neural Network Model Considering External Factors

  • 최현욱 (부산대학교 사회환경시스템공학과) ;
  • 이성행 (부산대학교 사회환경시스템공학과) ;
  • 문성우 (부산대학교 사회환경시스템공학과)
  • Choi, Hyun-Uk (Department of Civil and Environmental Engineering, Pusan National University) ;
  • Lee, Seong-Haeng (Department of Civil and Environmental Engineering, Pusan National University) ;
  • Moon, Sungwoo (Department of Civil and Environmental Engineering, Pusan National University)
  • 투고 : 2018.08.14
  • 심사 : 2018.12.07
  • 발행 : 2018.12.31
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초록

콘크리트 강도는 시멘트, 물, 자갈, 모래 그리고 혼화재 등 내부영향요인뿐만 아니라 실제 현장에서 발생하는 현장기온과 타설지연시간 등 외부영향요인의 영향을 받게 된다. 본 연구의 목적은 콘크리트 배합설계 시 내부영향요인과 외부영향요인을 고려하여 현장 콘크리트 타설시 최적의 콘크리트 강도를 확보하는 것이다. 본 연구에서는 내부영향요인과 외부영향요인에 대한 수준을 정의하고, 모두 24개의 조합에 대한 콘크리트 강도 테스트를 한 후 콘크리트 강도예측 뉴럴 네트워크 모델을 개발했다. 본 콘크리트 강도예측 뉴럴 네트워크 모델은 현장 콘크리트 타설 시 현장기온과 타설지연시간을 고려하여 콘크리트 강도를 예측하는 기능을 제공한다. 본 콘크리트 강도예측 뉴럴 네트워크 모델은 내부영향요인과 외부영향요인을 분석하고 실제 현장에서 콘크리트를 타설할 때 양생온도와 타설지연시간을 뉴럴 네트워크 입력변수로 처리하여 콘크리트 강도를 예측하는 기능을 제공한다. 시공사는 콘크리트 강도예측 결과를 활용하여 콘크리트 배합을 조정함으로써 현장타설 콘크리트 강도를 관리할 수 있을 것이다.

The strength of concrete is affected significantly not only by the internal influence factors of cement, water, sand, aggregate, and admixture, but also by the external influence factors of concrete placement delay and curing temperature. The objective of this research was to predict the concrete strength considering both the internal and external influence factors when concrete is placed at the construction site. In this study, a concrete strength test was conducted on the 24 combinations of internal and external influence factors, and a neural network model was constructed using the test data. This neural network model can predict the concrete strength considering the external influence factors of the concrete placement delay and curing temperature when concrete is placed at the construction site. Contractors can use the concrete strength prediction neural network model to make concrete more robust to external influence factors during concrete placement at a construction site.

키워드

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Fig. 1. Research procedure

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Fig. 2. Combination of concrete mix design

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Fig. 3. Neural network model architecture

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Fig. 4. Neural network training architecture in Matlab

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Fig. 5. Regression plot of neural network (X axis = target of neural network and Y axis = actual output received by neural network)

Table 1. Performance analysis considering internal and external variables in concrete mix design

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Table 2. Neural network data set for testing concrete performance

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