Comprehensive Machine Learning-Based Model for Predicting Compressive Strength of Ready-Mix Concrete.

ABSTRACT: Considering that compressive strength (CS) is an important mechanical property parameter in many design codes, in order to ensure structural safety, concrete CS needs to be tested before application. However, conducting CS tests with multiple influencing variables is costly and time-consuming. To address this issue, a machine learning-based modeling framework is put forward in this work to evaluate the concrete CS under complex conditions. The influential factors of this process are systematically categorized into five aspects: man, machine, material, method and environment (4M1E). A genetic algorithm (GA) was applied to identify the most important influential factors for CS modeling, after which, random forest (RF) was adopted as the modeling algorithm to predict the CS from the selected

SUBMITTER: Xu J 

PROVIDER: S-EPMC7956418 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature