This study aims to propose a simplified equation for estimating the bond strength of corroded reinforcing bars. To this end, extensive parametric analyses were performed using the detailed analysis method presented in the authors’ previous study, where a wide range of critical variables were considered, such as compressive strength of concrete, net cover thickness, and reinforcing bar diameter. The sensitivity in bond strength of the corroded reinforcing bar according to each variable was evaluated. On this basis, a simplified formula for the bond strength of the corroded reinforcing bar was derived through regression analysis. The proposed equation was rigorously tested and verified using the bond test results of corroded reinforcing bars collected from the literature. The results confirmed that the proposed equation could estimate the bond strengths of specimens with better accuracy than the existing models, providing a reliable tool for engineers and researchers. In addition, the proposed equation was used to analyze the development length required for corroded tensile reinforcement to exert its yield strength, and it showed that the cover thickness of concrete must be at least four times the diameter of the reinforcing bar to achieve the yielding strength of reinforcing bar even at a corrosion degree of more than 5.0%.

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.

고성능 콘크리트(HPC) 압축강도는 추가적인 시멘트질 재료의 사용으로 인해 예측하기 어렵고, 개선된 예측 모델의 개발이 필수적 이다. 따라서, 본 연구의 목적은 배깅과 스태킹을 결합한 앙상블 기법을 사용하여 HPC 압축강도 예측 모델을 개발하는 것이다. 이 논 문의 핵심적 기여는 기존 앙상블 기법인 배깅과 스태킹을 통합하여 새로운 앙상블 기법을 제시하고, 단일 기계학습 모델의 문제점을 해결하여 모델 예측 성능을 높이고자 한다. 단일 기계학습법으로 비선형 회귀분석, 서포트 벡터 머신, 인공신경망, 가우시안 프로세스 회귀를 사용하고, 앙상블 기법으로 배깅, 스태킹을 이용하였다. 결과적으로 본 연구에서 제안된 모델이 단일 기계학습 모델, 배깅 및 스태킹 모델보다 높은 정확도를 보였다. 이는 대표적인 4가지 성능 지표 비교를 통해 확인하였고, 제안된 방법의 유효성을 검증하였다.

The contemporary high-tech structures have become enlarged and their functions more diversified. Steel concrete structure and composite material structures are not exceptions. Therefore, there have been on-going studies on fiber reinforcement materials to improve the characteristics of brittleness, bending and tension stress and others, the short-comings of existing concrete. In this study, the purpose is to develop the estimated model with dynamic characteristics following the steel fiber mixture rate and formation ration by using the nerve network in mixed steel fiber reinforced concrete (SFRC). This study took a look at the tendency of studies by collecting and analyzing the data of the advanced studies on SFRC, and facilitated it on the learning data required in the model development. In addition, by applying the diverse nerve network model and various algorithms to develop the optimal nerve network model appropriate to the dynamic characteristics. The accuracy of the developed nerve network model was compared with the experiment data value of other researchers not utilized as the learning data, the experiment data value undertaken in this study, and comparison made with the formulas proposed by the researchers. And, by analyzing the influence of learning data of nerve network model on the estimation result, the sensitivity of the forecasting system on the learning data of the nerve network is analyzed.

Recently, the amount of the mineral admixture including fly ash and ground granulated blast-furnace slag was increased for the purpose of CO2 gas emission reduction in the concrete industry. However, in the case of korea, estimation model of strength development in concrete structural design code was prescribed a constant value according to cement type and curing method about the portland cement. therefore, the properties of strength development according to time of concrete using fly ash and ground granulated blast-furnace slag does not reflected estimation model of strength development. Accordingly, this paper was evaluated strength according to time on the concrete strength range using fly ash and ground granulated blast-furnace Slag and the strength development constant Bsc of concrete according to the kind of the mineral admixture and mixing ratio was proposed

FRP 판은 외부 부착된 보강 판의 효과적인 부착강도의 증진으로 실질적으로 부착강도에 대한 많은 연구가 수행되어왔다. 선행연구자들은 이러한 부착강도를 알아보기 위하여 다양한 변수를 설정하여 실험을 통하여 FRP 판의 부착강도를 규명하였다. 그러나, 이러한 부착강도를 알아보기 위한 실험은 장비구축의 비용과 시간 소비가 많이 되고 수행하기 어렵기 때문에 국한적으로 수행되고 있다. 본 연구는 선행연구자들의 부착실험 데이터를 다양한 신경망 모형과 알고리즘을 적용하여 최적의 인공신경망 모형을 개발하는데 그 목적이 있다. 인공신경망 모형의 출력층은 부착강도, 입력층은 FRP 판의 두께, 폭, 부착 길이, 탄성계수, 인장강도와 콘크리트의 압축강도, 인장강도, 폭을 변수로 선정하여 학습을 수행하였다. 개발된 인공신경망 모형은 역전파 학습 알고리즘을 적용하였으며, 오차는 0.001범위에 수렴되도록 학습을 하였다. 또한, 일반화 과정은 Bayesian 기법을 도입함으로써 보다 일반화된 방법으로 과대적합의 문제를 해소하였다. 개발된 모형의 검증은 학습에 이용되지 않은 다른 선행연구자들의 부착강도 결과 값과 비교함으로서 실시하였다.