To develop a computer software application that leverages Artificial Intelligence (AI) and Machine Learning (ML) for the visualization and implementation of engineering analysis of structures. The platform will provide actionable insights into structural health by analyzing key variables such as applied loads, strain, cracks, tilt, and corrosion. By combining engineering principles with AI/ML-driven analytics, the tool will empower stakeholders to make real-time, data-driven decisions to ensure the safety and longevity of structures.

1. Product Development Goals

• AI/ML Integration:
• Implement algorithms to analyze sensor data for detecting patterns, anomalies, and trends related to structural health.
• Develop predictive models for forecasting the lifespan and risk factors of structures.
• Data Visualization:
• Create interactive dashboards that allow stakeholders to visualize key metrics such as loads, strain, and tilt in real-time.
• Build heatmaps, graphs, and other visual elements to enhance user understanding.
• Engineering Analysis Modules:
• Design modules that simulate structural behavior under applied forces, strain, and environmental factors.
• Incorporate ML models to suggest preventive actions based on data trends.
• User-Friendly Reporting:
• Develop a feature for generating automated, downloadable reports that summarize findings and recommendations.

2. Core Deliverables

• Functional AI/ML Models:
• A working prototype of AI/ML algorithms capable of processing structural health data.
• Models for anomaly detection, classification of structural risks, and predictive analytics.
• Interactive Web Application:
• A user-friendly web platform with real-time visualization tools.
• A responsive interface optimized for desktops and tablets.
• Engineering Analysis Features:
• Implementation of analytical tools for tilt measurement, crack detection, and corrosion assessment.
• Comprehensive Reports:
• Automatic generation of reports summarizing structural health metrics, trends, and actionable recommendations.
• Documentation:
• Detailed technical documentation covering algorithms, system architecture, and user workflows.

3. Key Learning Outcomes for Learners

• AI/ML Proficiency:
• Hands-on experience designing and deploying AI/ML models for real-world applications.
• Understanding of feature engineering and model optimization for structural health monitoring.
• Data Analytics and Engineering Insights:
• Ability to process and analyze large datasets from sensors.
• Development of a cross-disciplinary approach by merging engineering principles with data science.
• Software Development:
• Experience in building end-to-end software solutions, from backend processing to frontend visualization.
• Knowledge of deploying applications in cloud environments.
• Product Management:
• Exposure to the product lifecycle, including requirement gathering, roadmap planning, and stakeholder communication.
• Collaboration with cross-functional teams to deliver on project goals.
• Communication and Documentation:
• Skills in writing user stories, workflows, and creating user manuals.
• Presentation of project outcomes and value to stakeholders.
• By the end of the project, learners will have developed a cutting-edge AI/ML-powered structural analysis tool and gained experience across product management, technical development, and cross-functional teamwork.