Morgan D. Sanger
  • Bio
  • Experience
  • Publications
  • Presentations
  • Teaching
  • Library
  • Publications
    • A database of cone penetration tests from North America (version 2)
    • A database of shear wave velocity tests from North America
    • AI-driven seismic velocity modelling in the North Sea: using onshore data to predict offshore conditions
    • Mechanics-informed machine learning for geospatial modeling of soil liquefaction: global and national surrogate models for simulation and near-real-time response
    • Pacific Northwest liquefaction hazard simulations
    • Informing predictions from above with data from below: a hierarchical geospatial liquefaction model for rapid response and simulation
    • Mechanics-informed machine learning for geospatial modeling of soil liquefaction: example model implementation in Jupyter Notebook and Matlab
    • Mechanics-informed machine learning for geospatial modeling of soil liquefaction: global model map products for LPI, LPIISH, and LSN
    • Mechanics-informed machine learning for geospatial modeling of soil liquefaction: New Zealand model map products for LPI, LPIISH, and LSN
    • Transportation infrastructure performance under seismic hazards with SimCenter Tools
    • A database of cone penetration tests from the Cascadia Subduction Zone
    • On the underutilization of artificial intelligence models in geotechnical practice
    • A database of cone penetration tests from North America
    • Why AI models for predicting soil liquefaction have been ignored, plus some that shouldn’t be
    • Meet Geo P. Tech, AI Chatbot Geotechnical Engineer: How AI Language Models Like 'ChatGPT' Could Change the Profession
    • Recycled concrete aggregate in base course applications: Review of field and laboratory investigations of leachate pH
    • Neutralization of high pH and alkalinity effluent from recycled concrete aggregate by common subgrade soil
    • Early introduction of STEM through sustainable engineering
    • Chemistry of recycled concrete aggregate leachate in pavement base course applications
    • Characterization of recycled concrete aggregate after eight years of field deployment
    • Fate and transport of recycled concrete aggregate leachate
    • Assessing the impact of contact time on leachate chemistry from recycled concrete aggregate
    • Environmental benefits of cold-in-place recycling
    • Environmental benefits of cold-in-place recycling
    • Revisiting geophysical interpretations of the Midcontinent Rift below Lake Superior
    • Seismic interpretation of the 1.1 Ga Midcontinent Rift volcanic interval beneath Lake Superior
    • Geospatial AI for liquefaction hazard and impact forecasting: a demonstrative study in the U.S. Pacific Northwest
    • Towards AI-driven seismic velocity modelling in the North Sea: Using onshore data to predict offshore conditions
  • Presentations
    • Liquefaction impacts on PacTrans mobility: Mechanics-informed AI modeling for simulation, disaster equity, and near-real-time response
    • Lessons in geotechnical earthquake engineering from large-magnitude earthquakes in Mexico
    • AI-driven seismic velocity modeling in the North Sea: using onshore data to predict offshore conditions
    • Integrating public and proprietary geotechnical data sources for improved parameter mapping
    • Mechanics-informed machine learning for geospatial modeling of liquefaction: global and national surrogate models for simulation and near-real-time response
    • Lessons from the graveyard: resurrecting AI for geohazard prediction in a riskier world
    • Why most AI models are ignored and why some probably shouldn’t be + geotechnical trends: Musings on education, research, and the future
    • Mechanics-informed machine learning for regional geohazard modeling
    • Application of AI/ML to large geospatial and geotechnical data sets for improved natural hazard modeling
    • A mechanics-informed machine learning model for regional liquefaction hazard planning and response
    • Rapid Map: A mechanics-informed machine learning model for regional liquefaction hazard planning and response
    • Mechanics-informed, geospatial machine learning for natural hazard planning and response
    • Why most “AI” models are ignored, plus some that probably shouldn’t be
    • Geosp-AI-tial liquefaction: mechanics-informed AI and geospatial data for liquefaction hazard planning and response
    • AI in geotechnical engineering practice
    • Navigating geohazards: AI-driven, near-real-time liquefaction predictions and insights for transportation networks
    • Introduction to generative AI and large language models
    • Drone usage
    • Geotechnical field investigation methods
    • Chemistry of recycled concrete aggregate leachate in pavement base course applications
    • Chemistry of recycled concrete aggregate leachate in pavement base course applications
    • Fate and transport of recycled concrete aggregate leachate
    • Assessing the impact of contact time on leachate chemistry from recycled concrete aggregate
    • Eva the Engineer: encouraging young women at the intersection of engineering and sustainability
    • Eva the Engineer: encouraging young women at the intersection of engineering and sustainability
    • Revisiting geophysical interpretations of the Midcontinent Rift below Lake Superior
    • Seismic interpretation of the 1.1 Ga Midcontinent Rift volcanic interval beneath Lake Superior
    • Environmental benefits of cold-in-place recycling
    • Environmental benefits of cold-in-place recycling
  • Library
    • Nexus: A Brief History of Information Networks from the Stone Age to AI by Yuval Noah Harari
    • The Big Dig by NPR
    • Video: The Future of Scientific Code in the Era of Generative AI
    • See what else I'm reading
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Mechanics-informed, geospatial machine learning for natural hazard planning and response

Mar 7, 2024·
Morgan D. Sanger
,
M. Geyin
,
B. W. Maurer
· 0 min read
Video
Last updated on Mar 7, 2024
← Rapid Map: A mechanics-informed machine learning model for regional liquefaction hazard planning and response Apr 12, 2024
Why most “AI” models are ignored, plus some that probably shouldn’t be Mar 1, 2024 →

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