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Submitted by p.narsavage on Sun, 02/23/2020 - 21:06
Date
Presented by
Robert Liang, Ph.D. P.E., Jack (Hui) Wang, Ph.D., and Xiangrong Wang, Ph.D., University of Dayton
Presented At

Midwest Geotechnical Conference, Columbus, Ohio, September 17-19, 2019

University of Dayton (UD) Team is working on data analytics

  • Seamlessly integrated into the DIGGS ecosystem (read DIGGS compliant data as part of the data analysis software)
  • Automatic geotechnical data interpretation and associated uncertainty quantification (AI: unsupervised learning - machine learning and pattern recognition)
  • Stochastic simulation for unexplored locations based on extracted statistical characteristics and spatial correlation from the sampling locations (random field and stochastic simulation)
  • Quantify "confidence level" of inferred geotechnical model for informed decision making (e.g., preliminary design and detailed reliability based design)
  • Export enhanced DIGGS XML file with above derived data/information for better visualization (using GIS software, AR/VR mixed reality) and/or downstream design (CAD software)

Liang 2019 AI Based Methods for Characterization of Geotechnical Site Investigation Data.pdf