A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

• Published in: Engineering with computers Vol. 37; no. 4; pp. 3329 - 3346
• Main Authors: Duan, Jin, Asteris, Panagiotis G., Nguyen, Hoang, Bui, Xuan-Nam, Moayedi, Hossein
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Classical Mechanics; Compressive strength; Computer Science; Computer-Aided Engineering (CAD; Concrete; Concrete aggregates; Construction engineering; Control; Environmental protection; Heuristic methods; Math. Applications in Chemistry; Mathematical and Computational Engineering; Mechanical properties; Original Article; Performance indices; Recycled materials; Sustainable development; Systems Theory; Hybrid artificial intelligence; ICA-XGBoost; Recycled aggregate concrete; Compressive strength; Green construction
• ISSN: 0177-0667; 1435-5663
• DOI: 10.1007/s00366-020-01003-0

Recycled aggregate concrete is used as an alternative material in construction engineering, aiming to environmental protection and sustainable development. However, the compressive strength of this concrete material is considered as a crucial parameter and an important concern for construction engin...