Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides an overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques.

1. Structural Resilience through SHM: A Critical Review. 2. Differential Evolution Algorithm: An analysis of more than two decades of application in structural damage detection (2001-2022). 3. Fatigue Assessment and SHM of Steel Truss Bridges. 4. Sensor-Based Structural Assessment of Ageing Bridges Sensor-Based Assessment . 5. Pile Integrity Assessment through A Staged Data Interpretation Framework. 6. Data-Centric Monitoring of Wind Farms: Combining Sources of Information. 7. From Structural Health Monitoring to Finite Element Modelling of Heritage Structures: The Medieval Towers of Lucca. 8. Development of adaptive LQG controller for Structural Control using Particle Swarm Optimization. 9. Application of AI Tools in Creating Datasets from A Real Data Component for Structural Health Monitoring. 10. Ambient Vibration Prediction of a Cable-Stayed Bridge by Artificial Neural Network. 11. Modeling Uncertainties by Data-Driven Bayesian Updating for Structural and Damage Detection. 12. Image Processing for SHM: The resilience of computer vision-based monitoring systems and their measurement. 13. Automatic SHM of road surfaces using Artificial Intelligence and Deep Learning. 14. Computer Vision-based Intelligent Disaster Mitigation from Two Aspects of Structural System Identification and Local Damage Detection.