AI Based Methods for Characterization of Geotechnical Site Investigation Data

Date

2019-09-18

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)