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 engineers regarding its application. In the present work, the 28-days compressive strength of recycled aggregate concrete is investigated through four artificial intelligence techniques based on a meta-heuristic search of sociopolitical algorithm (i.e. ICA) and XGBoost, called the ICA-XGBoost model. Based on performance indices, the optimum among these developed models proved to be ICA-XGBoost model. Namely, findings demonstrated that the proposed ICA-XGBoost model performed better than the other models (i.e. ICA-ANN, ICA-SVR, and ICA-ANFIS models) in estimating compressive strength of recycled aggregate concrete. The suggested model can be used in construction engineering in order to ensure adequate mechanical performance of the recycled aggregate concrete and allow its safe use for building purposes.