IoT-Driven Enhanced Transformer-Based Prediction of Rock Slope Stability - Umar, Ibrahim Haruna; Lin, Hang; Yang, Chaoyi; Fırat, Müge Elif (Geological Society of London, 2026)
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 ((Formula presented.) =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. © 2026 The Author(s). Published by The Geological Society of London. All rights, including for text and data mining (TDM), artificial intelligence (AI) training, and similar technologies, are reserved. For permissions: https://www.lyellcollection.org/publishing-hub/permissions-policy. Publishing disclaimer: https://www.lyellcollection.org/publishing-hub/publishing-ethics.
Keywords: Alarm systems; Artificial life; Data mining; Deep learning; Electric transformer testing; Integration testing; Learning systems; Mine safety; Safety factor; Slope protection; Slope stability; Complex geology; Critical challenges; Dynamic mining; Factors of safeties; Mining stress; Real-time prediction; Rock slope stability; Stability classification; Stress factors; Transformer modeling; Forecasting