Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI).

ABSTRACT: Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO₂) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m³), Alkaline activator (kg/m³), Fly ash (kg/m³), SP dosage (kg/m³), NaOH Molarity, Aggregate (kg/m³), Temperature (°C) and compressive strength as output parameter. Python programming is util

SUBMITTER: Dodo Y 

PROVIDER: S-EPMC10897462 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature