Name
Title | ||
|---|---|---|
Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams |
Abstract | ||
|---|---|---|
This study presents a novel artificial intelligence (AI) technique based on two support vector machine (SVM) models and symbiotic organisms search (SOS) algorithm, called “optimized support vector machines with adaptive ensemble weighting” (OSVM-AEW), to predict the shear capacity of reinforced-concrete (RC) deep beams. This ensemble learning-based system combines two supervised learning models—the support vector machine (SVM) and least-squares support vector machine (LS-SVM)—with the SOS optimization algorithm as the optimizer. In OSVM-AEW, SOS is integrated to simultaneously select the optimal parameters of SVM and LS-SVM, and control the coordination process of the learning outputs. Experimental results show that OSVM-AEW achieves the greatest evaluation criteria for coefficient of correlation (0.9620), coefficient of determination (0.9254), mean absolute error (0.3854 MPa), mean absolute percentage error (7.68%), and root-mean-squared error (0.5265 MPa). This paper demonstrates the successful application of OSVM-AEW as an efficient tool for helping structural engineers in the RC deep beams design process. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00366-019-00753-w | Engineering with Computers |
Keywords | DocType | Volume |
Shear strength, RC deep beams, Ensemble model, Symbiotic organisms search, Support vector machine | Journal | 36 |
Issue | ISSN | Citations |
3 | 0177-0667 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Doddy Prayogo | 1 | 64 | 4.66 |
| Min-Yuan Cheng | 2 | 174 | 19.84 |
| Yu-Wei Wu | 3 | 43 | 5.89 |
| Duc-Hoc Tran | 4 | 54 | 2.92 |