Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques.

ABSTRACT: Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict the 28-day compressive strength of steel fiber-reinforced concrete (SFRC), machine learning techniques, i.e., individual and ensemble models, were considered. For this study, two ensemble approaches (SVR AdaBoost and SVR bagging) and one individual technique (support vector regression (SVR)) were used. Coefficient of determination (R²), statistical assessment, and k-fold cross validation were carried out to scrutinize the efficiency of each approach used. In addition, a sensitivity technique was used to assess the influence of paramete

SUBMITTER: Li Y 

PROVIDER: S-EPMC9228203 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature