IoT-driven Enhanced Transformer-based prediction of rock slope stability

Real-time prediction of rock slope stability in active mines remains a critical challenge due to complex geology, dynamic mining stress and environmental factors. The Pulang Copper Mine, with its complex structural setting and ongoing subsidence, requires advanced monitoring to mitigate failure risk. This study aimed to develop and validate an IoT-driven Enhanced Transformer model for real-time prediction of the Factor of Safety (FoS) and stability classification, integrating numerical simulation, IoT data streaming and deep learning to improve early-warning capability. A FLAC3D simulation replicated two years of mining (730 daily steps) at six strategic monitoring points, generating time-series data for displacement, velocity, acceleration and FoS. An IoT framework streamed these data with