Module #1 Introduction to Structural Health Monitoring Overview of Structural Health Monitoring (SHM), its importance, and applications in civil engineering.
Module #2 Data Acquisition and Sources Introduction to data acquisition systems, types of sensors, and data sources for SHM.
Module #3 Data Preprocessing and Cleaning Techniques for data preprocessing, cleaning, and quality control for SHM data.
Module #4 Time Series Analysis Introduction to time series analysis, including trends, seasonality, and stationarity.
Module #5 Frequency Domain Analysis Introduction to frequency domain analysis, including Fourier transform and power spectral density.
Module #6 Signal Processing for SHM Advanced signal processing techniques for SHM, including filtering and denoising.
Module #7 Machine Learning Fundamentals Introduction to machine learning, including supervised and unsupervised learning.
Module #8 Anomaly Detection in SHM Machine learning-based anomaly detection methods for SHM.
Module #9 Damage Detection and Localization Methods for damage detection and localization in structures using machine learning and signal processing.
Module #10 Structural Health Monitoring using Statistical Process Control Introduction to statistical process control (SPC) for SHM, including control charts and hypothesis testing.
Module #11 Data Mining for SHM Data mining techniques for SHM, including clustering, decision trees, and regression analysis.
Module #12 Uncertainty Quantification in SHM Methods for quantifying uncertainty in SHM, including Bayesian inference and Monte Carlo simulations.
Module #13 Sensing Technologies for SHM Overview of sensing technologies used in SHM, including accelerometers, GPS, and vision-based systems.
Module #14 Data Fusion for SHM Methods for data fusion in SHM, including sensor fusion and data integration.
Module #15 Real-World Applications of SHM Case studies of SHM applications in civil engineering, including bridges, buildings, and dams.
Module #16 SHM for Condition Assessment and Rating Methods for condition assessment and rating using SHM data.
Module #17 Intelligent Systems for SHM Introduction to intelligent systems, including artificial neural networks and genetic algorithms, for SHM.
Module #18 Cyber-Physical Systems for SHM Introduction to cyber-physical systems for SHM, including IoT and cloud-based systems.
Module #19 Data Visualization for SHM Methods for data visualization in SHM, including statistical graphics and animated plots.
Module #20 Big Data Analytics for SHM Introduction to big data analytics for SHM, including Hadoop and Spark.
Module #21 SHM for Fatigue Life Prediction Methods for fatigue life prediction using SHM data.
Module #22 SHM for Seismic Hazard Assessment Methods for seismic hazard assessment using SHM data.
Module #23 SHM for Wind Engineering Methods for wind engineering using SHM data.
Module #24 SHM for Bridge Health Monitoring Case studies of SHM applications in bridge health monitoring.
Module #25 SHM for Building Health Monitoring Case studies of SHM applications in building health monitoring.
Module #26 SHM for Dam Health Monitoring Case studies of SHM applications in dam health monitoring.
Module #27 SHM for Other Infrastructures Case studies of SHM applications in other infrastructures, including pipelines and tunnels.
Module #28 SHM Data Management and Storage Best practices for managing and storing large datasets in SHM.
Module #29 SHM Software and Toolboxes Overview of software and toolboxes used in SHM, including MATLAB, Python, and LabVIEW.
Module #30 Course Wrap-Up & Conclusion Planning next steps in Data Analysis for Structural Health Monitoring career
