논문 상세보기
pp.205-213
The purpose of this study was to develop a more accurate model for predicting the in-situ compressive strength of concrete pavements using Internet-of-Things (IoT)-based sensors and deep-learning techniques. This study aimed to overcome the limitations of traditional methods by accounting for various environmental conditions. Comprehensive environmental and hydration data were collected using IoT sensors to capture variables such as temperature, humidity, wind speed, and curing time. Data preprocessing included the removal of outliers and selection of relevant variables. Various modeling techniques, including regression analysis, classification and regression tree (CART), and artificial neural network (ANN), were applied to predict the heat of hydration and early compressive strength of concrete. The models were evaluated using metrics such as mean absolute error (MAE) to determine their effectiveness. The ANN model demonstrated superior performance, achieving a high prediction accuracy for early-age concrete strength, with an MAE of 0.297 and a predictive accuracy of 99.8%. For heat-of-hydration temperature prediction, the ANN model also outperformed the regression and CART models, exhibiting a lower MAE of 1.395. The analysis highlighted the significant impacts of temperature and curing time on the hydration process and strength development. This study confirmed that AI-based models, particularly ANNs, are highly effective in predicting early-age concrete strength and hydration temperature under varying environmental conditions. The ability of an ANN model to handle non-linear relationships and complex interactions among variables makes it a promising tool for real-time quality control in construction. Future research should explore the integration of additional factors and long-term strength predictions to further enhance the model accuracy.
1. 서론
2. 콘크리트 성숙도 방법
2.1. 성숙도 계산 방법
2.2. 성숙도 기반 강도 추정 방법
3. 방법론
3.1. 모델 개발을 위한 데이터 수집 및 검토
3.2. 수화열 온도와 강도 예측 모델링
4. 모델링 결과
4.1. 수화열 온도 예측 모델링 결과
4.2. 압축 강도 예측 모델링 결과
5. 결론
REFERENCES
