Development of an AI and Statistical Based Hybrid Early Warning System for Slope Failures Using Field Monitoring Data
| Reference No. | 2026a026 |
|---|---|
| Type/Category | Grant for General Research-Workshop（Ⅰ） |
| Title of Research Project | Development of an AI and Statistical Based Hybrid Early Warning System for Slope Failures Using Field Monitoring Data |
| Principal Investigator | Hemanta Hazarika（Department of Civil Engineering, Graduate School of Engineering, Kyushu University・Professor） |
| Research Period |
June 26,2026. -
June 27,2026. |
| Keyword(s) of Research Fields | Early Warning System (EWS) , Slope Disaster Mitigation, Artificial Intelligence (AI) , Statistical Analysis, Mathematical Modeling, Field Monitoring |
| Abstract for Research Report |
Objectives: In recent years, the increasing severity of heavy rainfall events has significantly elevated disaster risks on slopes along roadways. Ensuring the safety of road users and maintaining traffic functionality have thus made the early detection of slope instability and the advancement of traffic regulation and evacuation decision-making urgent priorities. This study aims to develop a high-precision Early Warning System (EWS) based on field slope monitoring data, targeting not only embankment slopes but also high-risk cut slopes that pose significant threats to road functionality. The goal is to establish a practical disaster prevention technology that enables the identification of precursory signs prior to slope failure and supports decision-making for traffic control and emergency response. The originality of this research lies in the integration of observational data, soil mechanics, mathematical modeling, AI, and statistical analysis to provide a unified evaluation of slope behavior directly applicable to decision-making by road administrators. Monitoring data—including rainfall, soil moisture, pore water pressure, and internal slope displacement—will be collected in real time using low-cost LPWA (Low Power Wide Area) communication systems. Based on these data, precursory indicators useful for traffic regulation and disaster response decisions will be extracted through statistical analysis and AI-based methods. Expected Outcome: The outcomes of this research can be commercialized as an integrated solution combining sensor devices, communication modules, and cloud-based analytical systems. In particular, the proposed system has the potential to serve as a platform technology that creates new markets in the field of “Disaster Prevention × AI,” with prospects for international technology transfer and collaborative development. Furthermore, by promoting standardization of the system (e.g., JIS standardization) and developing implementation guidelines for local governments, it will be possible to accelerate widespread adoption and deployment from an institutional perspective. Overall, this research is expected to support innovation in disaster prevention technologies grounded in field needs and to serve as a model case for social implementation through collaboration among academia, industry, and government, contributing to the realization of sustainable and effective regional disaster resilience infrastructure. |
| Organizing Committee Members (Workshop) Participants (Short-term Joint Usage) |
Masanori Murai（Shimizu Corporation, Tokyo・Chief Engineer） Shiro Ohta（Kawasaki Geological Engineering Co., Ltd., Tokyo・Senior Managing Director） Takamasa Yamaji（Kawasaki Geological Engineering Co., Ltd., Fukuoka・General Manager） Kenta Mizuno（Wakachiku Construction Co., Ltd., Chiba・Group Leader） Masayuki Oishi（Daiki Rika Kogyo Co., Ltd., Saitama・CEO） Minoru Sakata（Space Engineering Co., Ltd., Fukuoka・Managing Director） Takashi Fujishiro（GeoDisaster Prevention Institute, Kita Kyushu・Director） Haruichi Kanaya（Graduate School of System Science and Electrical Engineering, Kyushu University・Professor） Osamu Takiguchi（Alsense Co., Ltd., Sagamihara・President） Kei Hirose（Institute of Mathematics for Industry, Kyushu University・Professor） Yasuhide Fukumoto（Institute of Mathematics for Industry, Kyushu University・Professor） Bui Trong Vinh（Research Institute for Sustainable Energy （RISE）, Ho Chi Minh University of Technology, Vietnam・Director） Lan Mai-Cao（Department of Drilling & Production Engineering, Ho Chi Minh University of Technology, Vietnam・Professor and Head） |