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.

Environmental impact assessment survey reflecting farmers` opinion on the residence and production space in rural settlement area by ORD showed that more than 86% of respondents thought their reservoirs and waterways (small rivers) were getting seriously contaminated primarily by garbage and livestock manure. A typical rural settlement unit was taken to assess the impact of improper management of livestock manure in the farms on the water quality of small river flowing down along the villages where swine and dairy farms were situated in Daejook 2, 3-ri, Seolseong-myun, Icheon-gun. Nitrogen compounds such as NO3-N, NO2-N, NH3-N, and phosphorus compound HxPO4, DO, BOD5, COD, and microbial density were analyzed to evaluate water quality at five test sites designated along the water stream. Tests showed. for example, BOD5 at site 4 was average 9.2mg/l which was about 3~8 times higher than that of observation site 2 and 3, at which most livestock houses were situated. This is a clear evidence that the nutrients of livestock manure illegally discharged to small river can lead to an eutrophication of the river at downstream. A soil absorption system with aeration could be one of alternatives to treat the contaminated wastewater by livestock manure. The place at downstream, inbetween observation site 1 and 2, could be the best construction site for the treatment facility from the standpoint of the overall treatment efficiency, An enclosed composting system can also be regarded as a good alternative for treatment of the sludge which is the by-product of the soil absorption system operation.