The Geo-Environment Laboratory conducts research focused on the interaction between soil structures—such as river levees, embankments, and natural slopes—and water, aiming to realize disaster-resilient social infrastructure. In recent years, seepage failure of embankments and slope failures caused by torrential rains and earthquakes have increased. We are working to elucidate the failure mechanisms and enhance safety using numerical analysis and field observations.
Another key theme is evaluating ground deformation caused by snowmelt infiltration and freeze-thaw cycles in snowy, cold regions. Furthermore, we are developing slope failure prediction models utilizing DEM, geological, and rainfall data, and advancing methods for assessing the integrity of earth structures incorporating AI technology. Additionally, related to geological disposal of radioactive waste, we evaluate the mechanical and swelling properties of bentonite buffer materials and predict long-term behavior through coupled analysis (THMC analysis) addressing interactions with rock formations.
Through these research efforts, we aim to enhance regional disaster resilience and contribute to building safe and sustainable social infrastructure.
Associate Professor : Shinichi KANAZAWA
Research Assistant: Yoshinori HOSAKA
Project Professor : Satoru OHTSUKA
Technical Staff: Satoe SUZUKI
⭐Study on rainfall infiltration behavior of soil structures considering vegetation's slope failure prevention function
⭐Study on techniques for reducing overflow and seepage risks in levees through various countermeasures
⭐Stability evaluation of embankments considering construction processes
⭐Evaluation of high-temperature effects on the mechanical properties of bentonite buffer materials
⭐Study on the internal structure of bentonite buffer materials using X-ray CT
⭐Development and enhancement of a long-term coupled thermal/ soil/ water/ air finite element analysis code
⭐Proposal of a levee breach prediction model using machine learning for large-scale flood risk assessment
⭐Development and enhancement of a slope failure prediction system using machine learning
⭐Development of a machine-learning-based simplified IRI evaluation method using acceleration response analysis
⭐Verification of large-scale data through high-accuracy improvement of liquefaction prediction models using machine learning
〒950-2181
8050,Ikarashi 2-no-cho, Nishi-ku, Niigata-shi, Niigata, Japan
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