Artificial Intelligence for Highway Geological Hazards Prediction and Prevention: Advances, Challenges and Future Directions

Highways are critical transportation infrastructures that play a vital role in economic development and regional connectivity. However, frequent geological hazards pose significant threats to highway safety and resilience. With the rapid expansion of highway networks and the continuous accumulation of multi-source data, improving the efficiency and accuracy of hazard identification and prediction has become a major challenge in highway geological hazard prevention and mitigation. This paper systematically reviews the development of geological hazard assessment and prediction methods, recent advances in artificial intelligence (AI) application. Based on this review, an AI-enabled framework for highway geological hazard risk identification and control is proposed. The framework establishes a multi-source database by integrating engineering design documents and historical hazard information. AI models are then trained to learn from these data and identify potential hazard risks along highway, enabling risk identification from line-level to site-level. For the identified high-risk sites, Newton-force monitoring devices are deployed to provide sensitive detection of mechanical changes. Based on accurate forecasting results, proactive measures, including early warning, preventive reinforcement, and adaptive control, can then be implemented. The proposed framework combines the large-scale analytical capabilities of AI with the high sensitivity of physics-based monitoring. It can promote the transition of highway geological hazard management from reactive response to proactive prevention, providing a systematic solution for enhancing the safety and resilience of highway lifeline infrastructure.