Using Machine Learning Algorithms to Estimate the Compressive Property of High Strength Fiber Reinforced Concrete.

ABSTRACT: The low tensile strain capacity and brittle nature of high-strength concrete (HSC) can be improved by incorporating steel fibers into it. Steel fibers' addition in HSC results in bridging behavior which improves its post-cracking behavior, provides cracks arresting and stresses transfer in concrete. Using machine learning (ML) techniques, concrete properties prediction is an effective solution to conserve construction time and cost. Therefore, sophisticated ML approaches are applied in this study to predict the compressive strength of steel fiber reinforced HSC (SFRHSC). To fulfil this purpose, a standalone ML model called Multiple-Layer Perceptron Neural Network (MLPNN) and ensembled ML algorithms named Bagging and Adaptive Boosting (AdaBoost) were employed in this study. The considered p

SUBMITTER: Dai L 

PROVIDER: S-EPMC9267573 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature