Research Projects

This research aims to fill critical gaps in the existing knowledge base surrounding autonomous driving technologies under challenging geological conditions.

This proposal outlines a project integrating traditional geotechnical engineering with AI-driven techniques to address key challenges in the field. The research focuses on studying deep-sea energy soils, developing risk assessment models, and utilizing AI methods such as deep learning and graph neural networks for predictive modeling. The goal is to enhance hazard prediction accuracy and provide practical solutions for issues like natural gas hydrate extraction and submarine slope stability, ultimately improving disaster prevention and risk management strategies in geotechnical and environmental engineering.

This research proposal aims to integrate artificial intelligence (AI) techniques with granular modeling to investigate rockburst failures in jointed rock masses exhibiting anisotropic properties. The project seeks to enhance the understanding of rockburst mechanisms by using AI to optimize simulation parameters, identify critical factors influencing rockburst risks, and improve the predictive accuracy of models. The focus will be on incorporating the influence of anisotropic joints, including variations in orientation, spacing, and mechanical properties, into the modeling framework. By combining granular modeling with AI-driven insights, this study aims to advance risk assessment and mitigation strategies for underground excavation projects.

VEC framework that leverages geospatial and geological insights to dynamically allocate computational tasks, improving the overall efficiency and reliability of intelligent transportation systems.

Landslides pose significant risks to infrastructure, human lives, and the environment, making accurate hazard assessment critical for disaster prevention and mitigation. Traditional methods often struggle with the inherent uncertainties in geological conditions, rainfall patterns, and slope characteristics. This proposal aims to develop a comprehensive stochastic hazard assessment framework that leverages machine learning and AI-driven techniques to improve predictive modeling accuracy and risk evaluation for landslides.

This research aims to explore the integration of uncertainty modeling and machine learning techniques to advance geotechnical engineering practices.

This research aims to fill critical gaps in the existing knowledge base surrounding autonomous driving technologies under challenging geological conditions.