PURPOSES : To enhance the accuracy of predicting the compressive strength of practical concrete mixtures, this study aimed to develop a machine learning model by utilizing the most commonly employed curing age, specifically, the 28-day curing period. The training dataset consisted of concrete mixture sample data at this curing age, along with samples subjected to a total load not exceeding 2,350 kg. The objective was to train a machine learning model to create a more practical predictive model suitable for real-world applications. METHODS : Three machine learning models—random forest, gradient boosting, and AdaBoost—were selected. Subsequently, the prepared dataset was used to train the selected models. Model 1 was trained using concrete sample data from the 28th curing day, followed by a comprehensive analysis of the results. For Model 2, training was conducted using data from the 28th day of curing, focusing specifically on instances where the total load was 2,350 kg or less. The results were systematically analyzed to determine the most suitable machine learning model for predicting the compressive strength of concrete. RESULTS : The machine learning model trained on concrete sample data from the 28th day of curing with a total weight of 2,350 kg or less exhibited higher accuracy than the model trained on weight-unrestricted data from the 28th day of curing. The models were evaluated in terms of accuracy, with the gradient boosting, AdaBoost, and random forest models demonstrating high accuracy, in that order. CONCLUSIONS : Machine learning models trained using concrete mix data based on practical and real-world scenarios demonstrated a higher accuracy than models trained on impractical concrete mix data. This case illustrates the significance of not only the quantity but also the quality of the data during the machine learning training process. Excluding outliers from the data appears to result in better accuracy for machine learning models. This underscores the importance of using high-quality and practical mixed concrete data for reliable and accurate model training.

1. 서론
2. DATA 준비
3. 연구방법
    3.1. Random Forest
    3.2. Gradient Boosting
    3.3. AdaBoost
4. 모델 개발
5. Results and Discussion
    5.1. 재령 28일 콘크리트 data 활용 모델 (모델 1)
    5.2. 재령 28일 콘크리트 data 중 샘플 총중량 2350kg이하 data 활용 모델 (모델 2)
    5.3. Model 1과 Model 2의 비교
6. 결론
REFERENCES

• 손재호(홍익대학교 건축공학부 교수) | Son Jaeho (Ph.D School of Architectural Engineering, Hongik University 2639 Sejong-ro, Jochiwon-eup, Sejong 30016, Korea) Corresponding author