A novel approach for resilient modulus prediction using extreme learning machine-equilibrium optimiser techniques

ABSTRACT

This study presents a novel hybrid intelligent approach using Extreme Learning Machine (ELM) and Equilibrium Optimiser (EO) (ELM-EO) for predicting resilient modulus, M_r of Unbound Granular Materials (UGMs). Fourteen various blends of Recycled Concrete Aggregate (RCA) with Recycled Clay Masonry (RCM), and Electric Arc Furnace Steel (EAFS) slag with limestone aggregates were tested in the laboratory using routine and advanced tests. The laboratory M_r testing produced 224 measurements based on the average of triplicate samples for each blend. The performance of the ELM-EO approach was evaluated and compared with conventional regression, ELM-biogeography-based optimisation (BBO) (ELM-BBO) and ELM-genetic algorithm (ELM-GA) approaches using the same input properties. The inputs used for the M_r prediction are the bulk stress, percent of RCM, and/or percent of EAFS. The results demonstrate that the performance of ELM-EO and ELM-BBO approaches is better than ELM-GA and regression approaches for predicting M_r. The overall statistical measures of the proposed approaches show that the ELM-EO approach ranks first as it outperforms the other approaches with coefficient of determination (R²) of 0.924 and Root Mean Square Error (RMSE) of 37.08 MPa.

KEYWORDS:

• Resilient modulus
• sustainability
• modeling
• ELM
• equilibrium optimiser

Acknowledgements

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20NANO-B156177-01).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport [grant number 20NANO-B156177-01].