Our mission is to solve challenging problems in the field of geotechnical engineering, aiming to enhance sustainability1 and resiliency2 of infrastructures and urban systems.
We use the following two approaches to full our mission:
Physics based approach: Utilization of high-fidelity and large scale numerical simulations
Data driven procedure: Utilization of supervised and unsupervised machine learning algorithms to develop artificial intelligence models.
Our research topics can be categorized into two themes: 1) Geotechnical earthquake engineering and 2) Offshore geotechnology. Detailed information about each research theme and associated projects is provided in the Research Topic section. The topics of our research projects are diverse, and many are not directly correlated. However, we emphasize that these projects are not short or disconnected; instead, they often span extended periods, funded through a series of science foundations, government institutions, and industry projects.
While we employ advanced numerical tools in many projects, our ultimate goal is to apply the research outcomes in practice. We strive to integrate fragmented element-level research components into system-level simulations, providing predictions of system or societal fragility through collaborative and interdisciplinary efforts.
Currently, our focus is on developing data-driven procedures that will enhance the contemporary geotechnical design framework. Machine learning is most often employed as an alternative tool for regression. We seek to develop models that enable us to provide realistic and probabilistic predictions in seconds. We envison that rigorously trained machine learning-based models may eventually replace numerical tools in design and analysis of geosystems, thereby transforming the design methodology.