Data Driven Methods for Civil Structural Health Monitoring and Resilience

Latest Developments and Applications

Mohammad Noori, Carlo Rainieri, Marco Domaneschi, Vasilis Sarhosis, Mohammad Noori, Carlo Rainieri, Marco Domaneschi, Vasilis Sarhosis, Wael A. Altabey

• Publisher: CRC Press
• Year: 2023
• Print ISBN:
  9781032308371
• eBook ISBN:
  9781000965582
• Topic: Technology & Engineering
• Subtopic: Civil Engineering
• Index: Technology & Engineering

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.

Table of contents

• Cover Page
• Half-Title Page
• Series Page
• Title Page
• Copyright Page
• Contents
• Foreword
• A Brief Overview of the Book by the Editors
• About the Editors
• List of Contributors
• Chapter 1 Structural Resilience through Structural Health Monitoring: A Critical Review
• Chapter 2 The Differential Evolution Algorithm: An Analysis of More than Two Decades of Application in Structural Damage Detection (2001–2022)
• Chapter 3 Fatigue Assessment and Structural Health Monitoring of Steel Truss Bridges
• Chapter 4 Sensor-Based Structural Assessment of Aging Bridges
• Chapter 5 Pile Integrity Assessment through a Staged Data Interpretation Framework
• Chapter 6 Data-Centric Monitoring of Wind Farms: Combining Sources of Information
• Chapter 7 From Structural Health Monitoring to Finite Element Modeling of Heritage Structures: The Medieval Towers of Lucca
• Chapter 8 Development of an Adaptive Linear Quadratic Gaussian (LQG) Controller for Structural Control Using Particle Swarm Optimization
• Chapter 9 Application of AI Tools in Creating Datasets from a Real Data Component for Structural Health Monitoring
• Chapter 10 Ambient Vibration Prediction of a Cable-Stayed Bridge by an Artificial Neural Network
• Chapter 11 Modeling Uncertainties by Data-Driven Bayesian Updating for Structural and Damage Detection
• Chapter 12 Image Processing for Structural Health Monitoring: The Resilience of Computer Vision–Based Monitoring Systems and Their Measurement
• Chapter 13 Automatic Structural Health Monitoring of Road Surfaces Using Artificial Intelligence and Deep Learning
• Chapter 14 Computer Vision–Based Intelligent Disaster Mitigation from Two Aspects of Structural System Identification and Local Damage Detection
• Index