Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive 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. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.

chapter 2|44 pages

The Differential Evolution Algorithm

An Analysis of More than Two Decades of Application in Structural Damage Detection (2001–2022)

chapter 12|18 pages

Image Processing for Structural Health Monitoring

The Resilience of Computer Vision–Based Monitoring Systems and Their Measurement