The impact of a PEER funded research project "Deep Learning-Based Surrogate Modeling for Uncertainty Quantification in Soil-Structure Interaction Problems" is highlighted below. The project Principal Investigator (PI) is Elnaz Seylabi, Assistant Professor of Civil and Environmental Engineering, University of Nevada, Reno.
Download the Research Project Highlight which includes the abstract (PDF)
Research Impact
The ability to accelerate forward UQ in SSI problems can significantly improve our understanding of how uncertainties in the soil properties and the seismic wave-field affect the key statistics in the structural response and the surrounding soil. We expect to use achieved insights from this one-year project to lay out a roadmap for future research on developing practical and affordable methodologies for performance-based engineering of large-scale SSI and wave propagation problems in transportation systems. This, in turn, will create new opportunities to evaluate the methods adopted in practice for the analysis and design of such problems.
