Scour Protection Damage Assessment Tool

A Streamlit application for analyzing and visualizing damage to scour protection layers during offshore wind farm construction, with a focus on jack-up leg footprints.

Overview

This tool processes bathymetric data before and after construction events to:

1. Identify and quantify scour protection damage
2. Calculate volume of material displacement
3. Classify damage severity
4. Identify and analyze leg footprints
5. Visualize results with 2D/3D plots

Data Storage

This project uses a local PostgreSQL database and local files for development environment.

• Local files path: src/utils/source_files
• Database files path: src/utils/database
• Database: PostgreSQL 15 (deployed via Docker)

Database Setup

The application uses a local PostgreSQL database that is automatically deployed with Docker Compose.

Using Docker Compose (Recommended)

1. Copy the environment file:

cp .env.example .env

2. Start the services:

docker-compose up -d

This will start:

• PostgreSQL database on port 5432
• Streamlit application on port 8501

The database schema will be automatically initialized from src/utils/database/DDL.sql.

Manual Database Setup (Alternative)

If you prefer to run PostgreSQL manually:

1. Install PostgreSQL 15
2. Create a database named scour_analysis
3. Copy .env.example to .env and update the database connection settings
4. Run the schema initialization:

psql -d scour_analysis -f src/utils/database/DDL.sql

Features

• Data Processing: Handles various bathymetry formats (.csv, .xyz, .geotiff)
• Damage Quantification: Calculates volume of removed material and affected area
• Footprint Detection: Identifies and analyzes jack-up leg footprints
• Classification: Categorizes damage into low, medium, and high severity
• Visualization: Interactive 2D/3D plots of bathymetry and damage
• Reporting: Export analysis results

Installation

1. Clone this repository:

git clone https://github.com/yourusername/scour_analysis.git
cd scour_analysis

2. Create a virtual environment and install dependencies:

# Using conda
conda create -n scour_env python=3.9
conda activate scour_env

# Or using venv
python -m venv scour_env
source scour_env/bin/activate  # On Windows: scour_env\Scripts\activate

# Install requirements
pip install -r requirements.txt

Requirements

• Python 3.8+
• Streamlit
• NumPy
• Pandas
• Matplotlib
• Plotly
• SciPy
• scikit-learn
• PyYAML
• Rasterio

Create a requirements.txt file with:

streamlit>=1.22.0
numpy>=1.20.0
pandas>=1.3.0
matplotlib>=3.5.0
plotly>=5.10.0
scipy>=1.7.0
scikit-learn>=1.0.0
pyyaml>=6.0
rasterio>=1.3.0

Usage

1. Run the Streamlit app:

cd scour_analysis
streamlit run app.py

2. Access the app in your browser (typically at http://localhost:8501)

3. Use the app:

   • Adjust analysis parameters
   • View the results and visualizations
   • Export data and reports

Data Format

The application accepts the following data formats:

Bathymetry Data

• CSV/XYZ files with columns for x, y, z coordinates
• GeoTIFF raster files

LPA (Leg Penetration Assessment) Data

• CSV with leg ID, position (x, y), penetration depth, radius, and date

Metocean Data

• CSV with date, wave height, wave direction, and current speed

Configuration

Seting the authentication

To setup the authentication, just descomment the Authentication Blocks on the app.py file.

Example:

# ----- AUTHENTICATION BLOCK -----
# if st.session_state["authentication_status"]:
#     with st.container(key="default_page_container"):
#         default_page()
# elif st.session_state["authentication_status"] == False:
#     st.error('Username/password is incorrect')

Create new users

To create new user just add a new object the users.yaml file

credentials:
  usernames:
    [...]
    new-user:
      email: email@example.com
      client_id: client_id
      failed_login_attempts: 0 # Will be managed automatically
      first_name: FirstName
      last_name: LastName
      logged_in: False # Will be managed automatically
      password: new-password # Will be hashed automatically

Database implementation

Run the docker compose file to setup the databse locally.

The config.yaml file contains settings for:

• Analysis parameters (resolution, thresholds)
• Visualization settings
• Sample data paths
• Coordinate reference system information

Contributors

• Alberto Ribas Marcondes Forcelius (alberto@forcelius.com)
• Luciana Paiva das Neves (-)

Acknowledgments

• Offshore Wind Industry
• Data providers