Ensemble Soft Computing Models for Prediction of Deflection of Steel–Concrete Composite Bridges

• Published in: Arabian journal for science and engineering (2011) Vol. 49; no. 4; pp. 5505 - 5515
• Main Authors: Le, Manh Van, Nguyen, Dam Duc, Ha, Hoang, Prakash, Indra, Pham, Binh Thai
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024; Springer Nature B.V
• Subjects: Composite bridges; Concrete bridges; Deflection; Engineering; Humanities and Social Sciences; multidisciplinary; Performance prediction; Prediction models; Research Article-Civil Engineering; Science; Soft computing; Steel bridges; Bridge engineering; Ensemble models; Soft computing; Vertical deflection
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-023-08474-5

The vertical deflection of steel–concrete composite bridges (VDCB) was estimated using novel ensemble soft computing (SC) models. These models, namely SGBE-RF, RSS-RF, and B-RF, are a combination of random forest and various ensemble techniques such as Stochastic Gradient Boosting (SGBE), random SUBSPACE (RSS), and bagging (B). Data from 83 bridges in Vietnam were obtained and utilized for the study. The importance of input variables used in prediction modeling was evaluated using correlation-based feature selection. The models were validated and compared using various methods including root mean squared error, mean absolute error, R -squared ( R 2 ), and Taylor diagrams. The validation results revealed that all three ensemble models, SGBE-RF ( R 2  = 0.805), RSS-RF ( R 2  = 0.781), and B-RF ( R 2  = 0.764) performed well in predicting the VDCB. Their performance surpassed that of the single RF model ( R 2  = 0.74). Among them, SGBE-RF emerged as the superior model. Therefore, it can be concluded that SGBE-RF is a powerful tool in accurate and quick prediction of the VDCB.