AI for Building Defect Detection

Here’s a requirements specification for the AI-based application to detect building defects in accordance with Australian Standards and the Australian Building Code: Project Name: Building Defect Recognition AI Application Objective: Develop an AI-powered application that allows users to identify potential building defects by uploading images or videos. The system will analyze the input, determine if a defect exists based on the Australian Standards and the Australian Building Code, and provide detailed information about the relevant code violations. Key Features and Requirements: 1. User Interface (UI): • Mobile App/Web App: • Simple, intuitive interface for non-technical users. • Upload functionality for images, videos, or text descriptions of building components. • Dashboard: • Display results of the analysis. • Highlight areas of concern in uploaded images. • Provide links to relevant sections of the Australian Standards and Building Code. 2. Core Functionality: • AI-Powered Defect Recognition: • Analyze images or videos using computer vision models. • Detect common building defects (e.g., cracks, water damage, structural misalignment, improper material use). • Categorize defects (e.g., cosmetic vs. structural). • Compliance Checking: • Cross-reference detected defects with Australian Standards and the Australian Building Code. • Identify specific codes or sections being contravened. • Defect Severity Assessment: • Assess the severity or urgency of each defect. • Suggest recommended actions for resolution. 3. Knowledge Base Integration: • Comprehensive database of Australian Standards and the Australian Building Code (e.g., NCC 2022). • Easy updates to database when standards are revised. 4. Technology Requirements: • AI/ML Models: • Pre-trained models for image recognition (e.g., TensorFlow, PyTorch). • Fine-tuned with data relevant to Australian construction standards. • Data Sources: • A curated dataset of annotated building defect images for training and validation. • Licensed access to Australian Standards and the Australian Building Code. • Software Stack: • Backend: Python (Django/Flask) or Node.js. • Frontend: React.js, Angular, or Vue.js for web; Flutter or React Native for mobile. • Cloud: AWS, Google Cloud, or Azure for AI processing and data storage. 5. Additional Features (Optional): • Integration with Registered Building Practitioners: • Allow users to submit identified defects to certified practitioners for expert review. • Report Generation: • Generate professional reports detailing defects, compliance issues, and suggested fixes. • Multi-Language Support: • Support for different languages or regional variations of standards. • Offline Mode: • Allow basic functionality in areas without internet connectivity. 6. Security and Privacy: • End-to-end encryption for data uploaded by users. • Adherence to Australian data protection laws (e.g., Privacy Act 1988). Deliverables: 1. Functional application (web/mobile). 2. Documentation: • User guide. • Technical documentation for maintenance. 3. AI training dataset and model files. 4. Testing results to verify accuracy and reliability. Timeline: • Initial development: 6 months (subject to adjustments based on scope). • Beta testing and feedback: 2 months. Budget Factors for Quotation: 1. Cost of AI/ML model development and training. 2. Licensing for Australian Standards and Building Code references. 3. Frontend and backend development. 4. Ongoing maintenance and updates. 5. Cloud storage and computational resources. Required Quote Details: 1. Total project cost. 2. Breakdown of costs by module (e.g., UI, AI, backend). 3. Estimated timeline for each development phase. 4. Details of any recurring costs (e.g., cloud services, licensing). This detailed specification will help programmers and engineers estimate the cost of the project and align their development efforts with your needs. Let me know if you’d like to refine it further!