Optimized ANN-based surrogate models for evaluating the stability of trapdoors in Hoek‒Brown rock masses

One of the main concerns of underground engineering works, such as subsurface structures and mining in rock formations, is ensuring their safety. The objective of this work is to present the stability analysis of trapdoors in Hoek-Brown (HB) rock masses, and to propose an innovative soft-computing approach utilizing optimized ANN-based surrogate models for evaluating the stability of trapdoors. The stability factor serves as a key parameter in formulating both lower bound (LB) and upper bound (UB) solutions for two-dimensional trapdoor through the finite element limit analysis (FELA). Furthermore, this paper introduces hybrid machine learning models that integrate artificial neural networks (ANNs) with diverse optimization algorithms (OAs), such as the ant lion optimizer (ALO), imperialist competitive algorithm (ICA), shuffled complex evolution algorithm (SCE), and teaching learning-based optimization (TLBO). Rigorous optimization ensures the accuracy and efficiency of these models in capturing the intricate dynamics of stability investigation. The performance of the proposed models is rigorously evaluated using metrics, convergence curves, regression plot, Taylor diagram, and rank analysis. Consequently, The ANN-SCE model achieved the highest performance (Testing Set), with R² of 0.9630, MAE of 2.7416, RMSE of 0.3696, VAF(%) of 96.2834, IOS of 0.0269, and RSR of 0.0172, respectively. These results demonstrate the accuracy and efficiency of the proposed models in capturing the complex dynamics of stability investigations. This research provides practical tools for engineers to assess road stability, plan mitigation for sinkholes, and account for rock strength using the Hoek-Brown criterion.