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A platform for research: civil engineering, architecture and urbanism
A novel approach for resilient modulus prediction using extreme learning machine-equilibrium optimiser techniques
This study presents a novel hybrid intelligent approach using Extreme Learning Machine (ELM) and Equilibrium Optimiser (EO) (ELM-EO) for predicting resilient modulus, Mr 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 Mr 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 Mr 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 Mr. 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 (R 2) of 0.924 and Root Mean Square Error (RMSE) of 37.08 MPa.
A novel approach for resilient modulus prediction using extreme learning machine-equilibrium optimiser techniques
This study presents a novel hybrid intelligent approach using Extreme Learning Machine (ELM) and Equilibrium Optimiser (EO) (ELM-EO) for predicting resilient modulus, Mr 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 Mr 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 Mr 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 Mr. 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 (R 2) of 0.924 and Root Mean Square Error (RMSE) of 37.08 MPa.
A novel approach for resilient modulus prediction using extreme learning machine-equilibrium optimiser techniques
Gabr, Alaa R. (author) / Roy, Bishwajit (author) / Kaloop, Mosbeh R. (author) / Kumar, Deepak (author) / Arisha, Ali (author) / Shiha, Mohamed (author) / Shwally, Sayed (author) / Hu, Jong Wan (author) / El-Badawy, Sherif M. (author)
International Journal of Pavement Engineering ; 23 ; 3346-3356
2022-08-24
11 pages
Article (Journal)
Electronic Resource
Unknown
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