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.

本研究提出了极限学习机 (ELM) 和自适应神经群智能 (ANSI) 技术的新颖集成，用于确定铁路轨道路基层土壤的加利福尼亚承载比 (CBR)，这是岩土工程的一个关键实时问题. 采用具有自适应和时变加速系数 (TAC) 的粒子群优化 (PSO) 来优化 ELM 的学习参数。开发了三种基于 ELM 的新型 ANSI 模型，即 ELM 耦合改进 PSO (ELM-MPSO)、ELM 耦合 TAC PSO (ELM-TPSO) 和 ELM 耦合改进 PSO (ELM-IPSO) 用于预测土壤的 CBR在浸泡条件下。与标准 PSO (SPSO) 相比，PSO 的修改和改进版本能够在早期迭代中收敛到高质量的解决方案。将所提出的模型与其他传统的软计算技术，如传统的 ELM、人工神经网络、遗传编程、支持向量机、数据处理的分组方法以及三种基于 ELM 的群智能优化模型（ELM-基于灰狼优化、基于 ELM 的粘菌算法和基于 ELM 的 Harris hawks 优化）。实验结果表明，所提出的基于 ELM 的 ANSI 模型可以获得最准确的预测，并确认 MPSO 优于 SPSO。考虑到所提出模型的后果和稳健性，可以得出结论，新构建的基于 ELM 的 ANSI 模型，尤其是 ELM-MPSO，