A novel integrated approach of ELM and modified equilibrium optimizer for predicting soil compression index of subgrade layer of Dedicated Freight Corridor

Abstract

This study proposes a high-performance machine learning model to sidestep the time of conducting actual laboratory tests of soil compression index (Cc), one of the important criteria for determining the settlement of subgrade layers of roadways, railways, and airport runways. The suggested method combines the modified equilibrium optimizer (MEO) and the extreme learning machine (ELM) in a novel way. In this study, Gaussian mutation with an exploratory search mechanism was incorporated to construct the MEO and used to enhance the performance of conventional ELM by optimizing its learning parameters. PCA (Principal component analysis)-based results exhibit that the developed ELM-MEO attained the most precise prediction with R2 = 0.9746, MAE = 0.0184, and RMSE = 0.0284 in training, and R2 = 0.9599, MAE = 0.0232, and RMSE = 0.0357 in the testing phase. The results showed that the proposed ELM-MEO model outperformed the other developed models, confirming the ELM-MEO model's superiority over the other models, such as random forest, gradient boosting machine, genetic programming, including the ELM and artificial neural network (ANN)-based models optimized with equilibrium optimizer, particle swarm optimization, Harris hawks optimization, slime mould algorithm, and marine predators algorithm. Based on the experimental results, the proposed ELM-MEO can be used as a promising alternative to predict soil Cc in civil engineering projects, including rail and road projects.