首页|Recent Studies from Don State Technical University Add New Data to Machine Learn ing (Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using M achine Learning Methods)
Recent Studies from Don State Technical University Add New Data to Machine Learn ing (Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using M achine Learning Methods)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on artificial intelligence have been published. According to news originating from Rostov on Don, Russia, b y NewsRx correspondents, research stated, "The determination of mechanical prope rties for different building materials is a highly relevant and practical field of application for machine learning (ML) techniques within the construction sect or." Funders for this research include Russian Science Foundation. Our news correspondents obtained a quote from the research from Don State Techni cal University: "When working with vibrocentrifuged concrete products and struct ures, it is crucial to consider factors related to the impact of aggressive envi ronments. Artificial intelligence methods can enhance the prediction of vibrocen trifuged concrete properties through the use of specialized machine learning alg orithms for materials' strength determination. The aim of this article is to est ablish and evaluate machine learning algorithms, specifically Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), CatBoost (CB), for th e prediction of compressive strength in vibrocentrifuged concrete under diverse aggressive operational conditions. This is achieved by utilizing a comprehensive database of experimental values obtained in laboratory settings. The following metrics were used to analyze the accuracy of the constructed regression models: Mean Absolute Error (* * MAE* * ), Mean Squared Error (* * MSE* * ), Root-Mean-S quare Error (* * RMSE* * ), Mean Absolute Percentage Error (* * MAPE* * ) and co efficient of determination (* * R* * 2). The average * * MAPE* * in the range from 2% (RF, CB) to 7% (LR, SVR) allowed us to draw conclusions about the possibility of using ‘smart' algorithms in the development of compositions and quality control of vibrocentri fuged concrete, which ultimately entails the improvement and acceleration of the construction and building materials manufacture."
Don State Technical UniversityRostov o n DonRussiaEurasiaAlgorithmsCyborgsEmerging TechnologiesMachine Lear ning
NETL
NSTL
