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 this data with <5-second latency. An Enhanced Transformer architecture with multi-head self-attention, multi-task learning ( λ =0.5), and advanced feature engineering was trained on the sequences. The Enhanced Transformer achieved superior performance, with testing R² ranging from 0.416 (Station 5, characterized by complex transitional kinematics) to 0.991 (Station 4). Testing MAE ranged [0.003596 (Station 2)–0.019071 (Station 5)], a reduction of up to 88% compared to the Standard Transformer. For four-class stability classification, the model attained a mean test accuracy of 0.993, with critical-class recall reaching 1.0—guaranteeing zero missed alarms for life-threatening critical and unstable conditions, the paramount objective for early-warning systems. The proposed IoT-Enhanced Transformer model provides a highly accurate, real-time solution for slope stability prediction, significantly outperforming conventional models.