Study of AI Based Methods for Characterization of Geotechnical Site Investigation Data

Due to the inadequate knowledge of the soil forming histories and/or human activities, the subsurface soil layers are difficult to ascertain. Subsurface uncertainty and its influence on geotechnical design have long been a challenge facing practitioners. Recently, the ASCE Geo-institute has developed the Data Interchange for Geotechnical and Geoenvironmental Specialists (DIGGS), which is a standard schema for transferring geotechnical data between multiple organizations. It paves the way of sharing and unifying datasets and forms a structural database for further data-driven modeling and analysis. The Office of Geotechnical Engineering (OGE) at Ohio Department of Transportation (ODOT) is taking a national leading role in supporting the development efforts of DIGGS and hence make this project possible. In this study, site investigation data in DIGGS format and archived format are jointly processed. An innovative technique developed by the research team has been further improved for better application in real-world projects. Bayesian machine learning is integrated with Markov random field models to infer and simulate subsurface models and geospatial data with quantified uncertainty. Spatial heterogeneity and statistical characteristics are modeled in terms of statistical and spatial patterns. These patterns serve as a basis to provide a synthesized interpretation of the soil profiles with uncertainty quantified. Four (4) validation projects have been performed in this report and the results are well documented. Summary and recommendations for future work are also provided. A short introduction of the key concepts behind this technique, and pathway for converting the existing program into a ready for implementation web-based program for potential ODOT usages are provided in the appendices.

• Record URL:
  https://ohiomemory.org/digital/collection/p267401ccp2/id/18553
• Record URL:
  https://rosap.ntl.bts.gov/view/dot/59022
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/1143389029
• Corporate Authors:

  University of Dayton

  Dayton, Ohio  United States  45469

  Ohio Department of Transportation

  Columbus, OH  United States  43216-0899

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Wang, Hui
  • Wang, Xiangrong
  • Liang, Robert
• Publication Date: 2020-2

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Technical Report
• Features: Appendices; Figures; Maps; Photos; Tables;
• Pagination: 56p

Subject/Index Terms

• TRT Terms: Artificial intelligence; Bayes' theorem; Geological surveying; Geotechnical engineering; Information processing; Markov processes; Soil profiles; Uncertainty; Validation
• Identifier Terms: Ohio Department of Transportation
• Subject Areas: Data and Information Technology; Geotechnology; Transportation (General);

Filing Info

• Accession Number: 01741949
• Record Type: Publication
• Report/Paper Numbers: FHWA/OH-2020-3
• Contract Numbers: SJN135785 Task#6
• Files: NTL, TRIS, ATRI, USDOT, STATEDOT
• Created Date: Jun 8 2020 2:15PM