Reports

Timeline Year 1: Foundation and Initial Development • Months 1-3: Literature Review and Proposal Refinement o Conduct an extensive literature review on SHM, digital twins, AI, and related technologies. o Refine the project proposal based on the latest research and feedback from advisors. • Months 4-6: System Design o Develop a comprehensive system design that integrates digital twin technology with advanced sensing technologies. o Create detailed blueprints outlining the architecture, components, and interactions within the SHM system. • Months 7-9: Sensor Deployment and Data Collection Setup o Identify suitable bridges for the study and obtain necessary permissions. o Deploy low-cost wireless accelerometers and other sensors on selected bridge structures. o Set up IoT technology for seamless data transmission from sensors to the central monitoring system. • Months 10-12: Initial Data Collection and Analysis o Begin collecting real-time data on vibrations, stress, and other relevant parameters. o Perform initial data analysis to understand the baseline structural health of the bridges. Year 2: Digital Twin Development and AI Integration • Months 13-15: Digital Twin Creation o Create a digital replica of the bridge using Building Information Modeling (BIM). o Ensure the digital twin updates in real-time based on data received from the sensors. • Months 16-18: Signal Processing and Data Preprocessing o Apply signal processing techniques to preprocess the sensor data. o Enhance data accuracy through filtering, noise reduction, and feature extraction. • Months 19-21: Machine Learning Model Development o Develop and compare various machine learning models for damage detection. o Select the most effective techniques for robust and accurate detection of structural anomalies. • Months 22-24: Integration and Testing o Integrate the digital twin with the collected data for real-time monitoring and visualization. o Test the system in a controlled environment to ensure functionality and accuracy. Year 3: DSS Development and Scalability • Months 25-27: Decision Support System (DSS) Development o Develop an intelligent DSS that integrates AI and data analysis for predictive maintenance and damage detection. o Design a user-friendly interface for engineers and maintenance personnel. • Months 28-30: System Validation and Optimization o Validate the system’s performance through extensive testing on different bridge structures. o Optimize the system based on feedback and performance metrics. • Months 31-33: Scalability and Application to Other Infrastructure Projects o Develop a scalable model that can be applied to other infrastructure projects. o Test the scalability of the system on different types of infrastructure. • Months 34-36: Final Review and Thesis Writing o Conduct a final review of the project, ensuring all objectives are met. o Write and submit the PhD thesis, incorporating all findings and contributions.

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