ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions

This study proposes novel integration of extreme learning machine (ELM) and adaptive neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio (CBR) of soils for the subgrade layers of railway tracks, a critical real-time problem of geotechnical engineering. Particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients (TAC) was employed to optimize the learning parameters of ELM. Three novel ELM-based ANSI models, namely ELM coupled-modified PSO (ELM-MPSO), ELM coupled-TAC PSO (ELM-TPSO), and ELM coupled-improved PSO (ELM-IPSO) were developed for predicting the CBR of soils in soaked conditions. Compared to standard PSO (SPSO), the modified and improved version of PSO are capable of converging to a high-quality solution at early iterations. A detailed comparison was made between the proposed models and other conventional soft computing techniques, such as conventional ELM, artificial neural network, genetic programming, support vector machine, group method of data handling, and three ELM-based swarm intelligence optimized models (ELM-based grey wolf optimization, ELM-based slime mould algorithm, and ELM-based Harris hawks optimization). Experimental results reveal that the proposed ELM-based ANSI models can attain the most accurate prediction and confirm the dominance of MPSO over SPSO. Considering the consequences and robustness of the proposed models, it can be concluded that the newly constructed ELM-based ANSI models, especially ELM-MPSO, can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering.