골재의 형상 특성과 인공신경망에 기반한 콘크리트 압축강도 예측 연구
Study on Prediction of Compressive Strength of Concrete based on Aggregate Shape Features and Artificial Neural Network
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
본 연구에서는 일반강도 범위 콘크리트의 단면에서 골재 형상의 특성을 추출하고 이를 인공신경망과 이미지 프로세싱 기술에 적용하여 콘크리트의 압축강도를 예측하였다. 이를 위하여 면적, 둘레, 길이 등과 같은 일반적인 골재 형상 특성과 함께 골재의 거리-각도 특징을 수치적으로 표현하고 물성치 예측에 활용하였다. 그 결과, 콘크리트 압축강도에 영향을 미치는 주요변수를 사용하지 않고 단면의 골재 형상 특성만을 사용하여 압축강도 예측이 가능하였으며, 인공신경망 알고리즘 구축을 통해 예측 강도와 실제 강도의 상대오차 4.43% 이내의 범위에서 콘크리트 압축강도를 예측할 수 있었다. 본 연구에서 도출된 결과를 기반으로 골재의 거리-각도 특징을 활용하여 콘크리트의 유동성, 휨·인장강도 등 다양한 특성을 예측도 가능할 것으로 판단된다.
Abstract
In this study, the concrete aggregate shape features were extracted from the cross-section of a normal concrete strength cylinder, and the compressive strength of the cylinder was predicted using artificial neural networks and image processing technology. The distance-angle features of aggregates, along with general aggregate shape features such as area, perimeter, major/minor axis lengths, etc., were numerically expressed and utilized for the compressive strength prediction. The results showed that compressive strength can be predicted using only the aggregate shape features of the cross-section without using major variables. The artificial neural network algorithm was able to predict concrete compressive strength within a range of 4.43% relative error between the predicted strength and test results. This experimental study indicates that various material properties such as rheology, and tensile strength of concrete can be predicted by utilizing aggregate shape features.
참고문헌 (12)
- The Status and future tasks of old buildings, "NARS", National Assembly Research Service, ~ (2020)
- 박보현, "딥러닝을 이용한 경량혼합토의 일축압축강도 예측 시스템", 한국구조물진단유지관리공학회 논문집, 24(3), pp.18~25 (2020), 한국구조물진단유지관리공학회
- Eskandari-Naddaf, H., "ANN Prediction of Cement Mortar Compressive Strength, Influence of Cement Strength Class", Construction and Building Materials, 138, pp.1~11 (2017)
- Tenza-Abril, A. J., "Prediction and Sensitivity Analysis of Compressive Strength in Segregated Lightweight Concrete Based on Artificial Neural Network Using Ultrasonic Pulse Velocity", Construction and Building Materials, 189, pp.1173~1183 (2018)
- Onyari, E. K., "Prediction of Compressive and Flexural Strengths of a Modified Zeolite Additive Mortar Using Artificial Neural Network", Construction and Building Materials, 187, pp.1232~1241 (2018)
- Ashrafian, A., "Prediction of Compressive Strength and Ultrasonic Pulse Velocity of Fiber Reinforced Concrete Incorporating Nano Silica Using Heuristic Regression Methods", Construction and Building Materials, 190, pp.479~494 (2018)
- Cascardi, A., "An Artificial Neural Networks Model for the Prediction of the Compressive Strength of FRP-Confined Concrete Circular Columns", Engineering Structures, 140, pp.199~208 (2017)
- Başyiǧit, C., "Assessment of Concrete Compressive Strength by Image Processing Technique", Construction and Building Materials, 37, pp.526~532 (2012)
- Dogan, G., "Concrete Compressive Strength Detection Using Image Processing Based New Test Method", Measurement : Journal of the International Measurement Confederation, 109, pp.137~148 (2017)
- Doǧan, G., "Statistical Feature Extraction Based on an Ann Approach for Estimating the Compressive Strength of Concrete", Neural Network World, 25(3), pp.301~318 (2015)
- Géron, A., "Hands-On Machine Learning with Scikit-Learn & TensorFlow", pp.336~339 (2017), O’Reilly Media, Inc
- "KS F 2405 Standard test method for compressive strength of concrete", ~