State-of-the-art advanced hybrid ANNs paradigm for assessment and prediction of slope stability

The study presents advanced hybrid artificial neural network (ANN) models to enhance the analysis of slope stability and the prediction of the factor of safety (FOS). Conventional methodologies, such as analytical procedures and numerical simulations, encounter difficulties in accurately representing intricate interactions. Consequently, machine learning (ML) simulation methods are being introduced as contemporary alternatives. This paper provides a comparative examination of hybrid artificial neural network (ANN) models utilizing several optimization algorithms (OAs) like Firefly (FF), Ant Lion (ALO), shuffled complex evolution (SCE), and the imperialist colony algorithm (ICA), simulated on a dataset of 349 slope cases. The performance of the ML models is enhanced by hyperparameter tuning and verified using various proven statistical indicators, error matrix, and external validation. It is concluded that all the applied models are robust for practical applications. The comparative analysis is carried out using rank analysis which proposes ANN-FF (rank = 64, R_TR² = 0.983 and R_TS² = 0.943) as the best performing model, followed by ANN-ICA (rank = 42, R_TR² = 0.9772 and R_TS² = 0.937) and ANN-ALO (rank = 37, R_TR² = 0.973 and R_TS² = 0.909). The developed hybrid ANN models demonstrate significant potential as a novel tool to aid engineers in estimating rock strain during the design phase of various engineering projects.