Particle Swarm Optimization Algorithm-Extreme Learning Machine (PSO-ELM) Model for Predicting Resilient Modulus of Stabilized Aggregate Bases
Source abstract
Abstract
Stabilized base/subbase materials provide more structural support and durability to both flexible and rigid pavements than conventional base/subbase materials. For the design of stabilized base/subbase layers in flexible pavements, good performance in terms of resilient modulus (Mr) under wet-dry cycle conditions is required. This study focuses on the development of a Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) to predict the performance of stabilized aggregate bases subjected to wet-dry cycles. Furthermore, the performance of the developed PSO-ELM model was compared with the Particle Swarm Optimization-based Artificial Neural Network (PSO-ANN) and Kernel ELM (KELM). The results showed that the PSO-ELM model significantly yielded higher prediction accuracy in terms of the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE), and the coefficient of determination (r2) compared with the other two investigated models, PSO-ANN and KELM. The PSO-ELM was unique in that the predicted Mr values generally yielded the same distribution and trend as the observed Mr data.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning and ELM · Infrastructure Maintenance and Monitoring · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
Incheon Disaster Prevention Research Center, Incheon National University, Incheon 22012, Korea
Public Works and Civil Engineering Department, Mansoura University, Mansoura 35516, Egypt
Department of Civil Engineering, National institute of Technology Patna, Patna 800005, India
Department of Computer Science and Engineering, National institute of Technology Patna, Patna 800005, India
Funding
Ministry of Science, ICT and Future Planning
References (52)
Show source-provided references
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