Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

• Published in: Materials Vol. 14; no. 4; p. 794
• Main Authors: Ahmad, Ayaz, Farooq, Furqan, Niewiadomski, Pawel, Ostrowski, Krzysztof, Akbar, Arslan, Aslam, Fahid, Alyousef, Rayed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.02.2021; MDPI
• Subjects: Algorithms; Bagging; Carbon dioxide; Cement industry; Civil engineering; Compressive strength; Concrete; Construction; Decision trees; Environmental impact; Fly ash; Gene expression; Greenhouse gases; Machine learning; Mechanical properties; Model accuracy; Optimization; Reinforced concrete; Root-mean-square errors; Sugarcane; Superplasticizers; Waste materials; concrete compressive strength; decision tree; DT-bagging regression; fly ash waste; ensemble modeling; cross-validation python

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accura...