DIGGS Analyzer

Team Members

Ground Decoder (University of Utah)

• Ripon Chandra Malo (Team Leader / PhD Student) - GitHub, Email
• Dr. Tong Qiu (Supervisor / Professor)
• Dr. Kami Mohammadi (Supervisor / Assistant Professor)

Challenge Theme(s)

This project addresses the following hackathon themes:

• Theme 1 — Data In/Data Out: Parses DIGGS XML files with automatic data extraction and supports multi-format export (Excel, CSV, JSON, PDF) with a built-in data converter module.
• Theme 2 — Visualization: Delivers interactive SPT boring logs, CPT profiles, grain-size curves, plasticity charts, 2D cross-section contour plots, and satellite-mapped borehole locations using Plotly and Mapbox.
• Theme 3 — Direct Design/Interpretation: Implements established geotechnical analysis methods including SPT-based Bearing Capacity, liquefaction assessment (Seed & Idriss / Youd et al.), Liquefaction Potential Index (Iwasaki), and foundation settlement calculations (Meyerhof, Schmertmann, Burland-Burbidge, consolidation).
• Theme 4 — Data Transformation: Converts DIGGS XML into structured tabular formats (Excel, CSV, JSON) and merges raw test data with contextual borehole information for downstream use.

Project Description

The DIGGS Analyzer is a powerful, modular Streamlit application designed for parsing, analyzing, and visualizing geotechnical data from DIGGS XML files. This tool seamlessly transforms complex geotechnical data into rich, intuitive visualizations, making data interpretation faster and more accessible for engineers and geologists.

Key features include an automated DIGGS parser, interactive Standard Penetration Test (SPT) and Cone Penetration Test (CPT) profiling, visual lithology tools using standard USCS colors, formatted Laboratory Test summary tables, liquefaction and settlement analysis modules, a 2D cross-section viewer with satellite mapping, and an AI-assisted interpretation assistant supporting Groq, Google Gemini, and Ollama backends.

Technologies Used

• Python
• Streamlit
• Plotly
• Pandas
• DIGGS XML Schema

Setup Instructions

Get up and running locally in a few simple steps. We recommend creating a virtual environment:

1. Navigate to the project directory:

   cd "DIGGS v2"

2. Create and activate a virtual environment:

   # On Mac/Linux
   python3 -m venv venv
   source venv/bin/activate
   
   # On Windows
   python -m venv venv
   venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Launch the Streamlit app:

   streamlit run app.py

Demo

Live demo