Affiliations: [a]
School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, China
| [b]
School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, China
| [c]
School of Information Technology, Nanchang Vocational University, Nanchang, Jiangxi, China
| [d]
School of Intelligent Construction, Luzhou Vocational and Technical College, Luzhou, Sichuan, China
Correspondence:
[*]
Corresponding author. Hongxing Sun, School of Information Technology, Nanchang Vocational University, Nanchang 330007, Jiangxi, China. E-mail:hongxingsun110@gmail.com.
Abstract: In the present study, three hybrid models include support vector regression-salp swarm optimization (SVR-SSO), support vector regression-biogeography-based (SVR-BBO), and support vector regression-phasor particle swarm optimization (SVR- PPSO) was applied to forecast pond ash’s CBR value modified with lime sludge (LS) and lime (LI). In the developed models, five variables were selected as inputs. It can result that the developed integrated models have R2 bigger than 0.9952. It means the agreement between observed and forecasted values by hybrid models is mainly similar to represent the highest accuracy. In both the training and testing stages, PSO-SVR results from better performance than the BBO-SVR model, with R2, RMSE, MAE, and PI equal to 0.9983, 0.6439, 0.3181, and 0.0081 for training data, and 0.9975, 0.7319, 0.4135, and 0.0141 for testing data, respectively. So, by considering the OBJ index, the OBJ value for PSO-SVR is 12.966, lower than BBO-SVR at 16.9957. Therefore, the PSO-SVR model outperforms another model to estimate the CBR of pond ash modified with LI and LS, consequently being recognized as the proposed model that makes it to be used for practical applications.
Keywords: California bearing ratio, phasor particle swarm optimization, biogeography-based optimization, salp swarm optimization, support vector regression
