🌉 StructurSense: The Infrastructure Guardian

Best Use of Gemini API - AI x WiC x SASE Hackathon

StructurSense is an AI-driven, web-based dashboard designed to promote a human-centered approach to infrastructure maintenance. By leveraging the Google Gemini 2.5 Flash API, this tool allows civil engineers and inspectors to upload images of infrastructure (concrete pillars, road surfaces, steel girders) and instantly receive a preliminary structural health and safety assessment.

🚀 Features

• Multimodal AI Analysis: Uses Gemini Vision to identify surface-level distress such as concrete spalling, exposed rebar, fatigue cracking, and steel corrosion.
• Severity Assessment: Automatically grades the damage (Low, Moderate, High, Critical) based on visual evidence.
• Conceptual Damage Index (D): Calculates a theoretical risk score to prioritize maintenance.
• Frictionless UI: Built entirely in Python using Streamlit for instant deployment and easy image uploading.

🛠️ Built With

• Python 3
• Google Gemini API (gemini-2.5-flash)
• Streamlit
• Pillow (PIL)

💻 How to Run Locally

1. Clone the repository:

   git clone https://github.com/rass89/StructurSense.git
   cd StructurSense
   
   

2. Create a virtual environment & install dependencies:

   python3 -m venv my_env
   source my_env/bin/activate
   pip install streamlit google-generativeai pillow
   Add your API Key:
   Open app.py and replace "YOUR_API_KEY_HERE" with your actual Google AI Studio key.
   

3. Launch the app:

   streamlit run app.py
   

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Inspiration: Current structural inspections often put engineers in dangerous, hard-to-reach environments. The inspiration behind StructurSense is to promote a human-centered approach to infrastructure maintenance. By using AI as a reliable assistant, we can analyze visual data before a human ever has to climb a bridge or scaffold. The goal is to keep inspectors safer and make the preliminary assessment process much faster and more accessible.

What it does: StructurSense is a web-based dashboard where users can upload images of infrastructure, such as concrete pillars, road surfaces, or steel bridge girders. The app uses the Gemini API to scan the image for signs of structural distress, instantly flagging issues like fatigue cracking, concrete spalling, or steel corrosion. It then generates an accessible, plain-language safety report.To quantify the risk, the tool helps conceptualize a simplified Damage Index ($D$) based on the identified severity: D = \sum_{i=1}^{n} (w_i x s_i) where w_i represents the structural weight or importance of the component, and s_i is the AI-assessed severity of the identified defect.

How we built it: We built the core logic using Python and integrated the gemini-2.5-flash model via Google AI Studio. Gemini's vast token limits and multimodal capabilities allowed us to feed it raw images alongside a strict prompt structure to return standardized, professional inspection reports. We rapidly prototyped the frontend using Streamlit to ensure a seamless image upload and reporting workflow.

Challenges we ran into: With a tight hackathon time limit, the biggest challenge was fine-tuning the prompt engineering. Initially, the model occasionally struggled to distinguish between harmless superficial water stains and actual concrete spalling. We had to iterate quickly, refining our prompts to guide Gemini's vision model to look for specific textural and geometric cues associated with critical corrosion and cracking.

Accomplishments that we're proud of: We are incredibly proud of taking a complex civil engineering challenge and turning it into a highly functional, user-friendly prototype in just a few hours. The accuracy of the multimodal analysis was impressive, successfully identifying rust and surface defects on our test images without requiring a custom-trained computer vision model.

What we learned: We learned just how powerful the Gemini API is for zero-shot image classification and analysis tasks. By providing the right context and constraints to the model, we could extract actionable, domain-specific insights immediately.

What's next for StructurSense: The immediate next step is expanding the model to handle continuous video streams. Ultimately, the long-term vision is to connect this AI backbone to drone feeds or augmented reality headsets for field inspectors, allowing them to see real-time, AI-generated overlays of structural health risks while they are on site.

Built With: gemini-api, github, google-ai-studio, mac-os, python, streamlit