Event Date
Abstract
Understanding subsurface conditions is critical to creating and maintaining resilient infrastructure systems, such as roads, dams, and levees. Current standards of practice for subsurface investigation can lead to an incomplete understanding of site conditions, particularly when the subsurface contains irregular bedrock topography, highly variable stratigraphy, voids, and/or localized effects of various natural hazards. To address this issue, geotechnical engineers have increasingly used geophysical methods to measure the shear wave velocity (VS) as a proxy for stiffness of near surface strata. In particular, surface wave methods such as the Multichannel Analysis of Surface Waves (MASW) have been developed within the last few decades as the demand for rapid and accurate VS profiles has increased in many civil engineering applications. Surface waves are often the strongest signals obtained from seismic geophysical testing, which simplifies their acquisition and incentivizes their use in near surface geotechnical settings. However, surface wave methods suffer from limitations related to data post processing, interpretation, and resolution. This presentation initially provides a discussion regarding the theoretical aspects of seismic geophysical testing as well as the fundamentals of data acquisition and post-processing. Case histories are briefly presented to demonstrate the strengths and limitations of MASW in particular. Finally, a discussion is provided of recent advances using full waveform tomography with high-performance computing (HPC) to increase resolution and better characterize the in-situ stiffness of geologic materials for applications related to natural hazards.
Biography
Dr. Joseph Coe is an Assistant Professor for the Department of Civil and Environmental Engineering at San José State University. Prior to joining San José State University, he held positions at The Citadel in Charleston, South Carolina, and Temple University in Philadelphia, Pennsylvania. He obtained his Civil Engineering Ph.D., M.S., and B.S. (Geology Minor) degrees from the University of California Los Angeles (UCLA). His career in geotechnical engineering spans eighteen years primarily as a researcher with nondestructive and geophysical imaging, instrumentation/sensor technology, signal processing, system identification, inverse problems and optimization, high-performance computing (HPC), and physics-based and data-driven stochastic modeling. Primary research interests focus on evaluation of urban natural hazards (seismicity, karst/sinkholes, bridge scour, landslides), reuse and rehabilitation of bridge foundations, sustainable geo materials, and use of big-data and artificial intelligence for modeling risk and resiliency of urban infrastructure systems.