Structural Health Monitoring of Bridges

As bridges age, bear the weight of growing traffic volumes, and are impacted by climate change, their structural integrity becomes an increasing concern. To offer a more precise, real-time solution for assessing the condition of bridges and to enable proactive maintenance strategies, Structural Health Monitoring of Bridges proposes an innovative approach for infrastructure assessment, focused on statistical pattern recognition (SPR) and advanced machine learning techniques.

The authors introduce a novel hybrid framework that integrates data-driven methodologies with advanced computational techniques, enabling more effective detection of faults and anomalies. By utilizing SPR and leveraging machine learning algorithms, this work provides fresh insights into how these modern tools can transform infrastructure monitoring, making it more efficient and responsive to evolving or newly emerging issues. Special attention is given to data processing techniques that allow the detection of damage patterns without relying on subjective human or destructive appraisal, significantly improving the accuracy and reliability of results.

By addressing both the technical and operational aspects of SHM, the book serves as an invaluable foundational reference resource to equip readers with the advanced knowledge and practical expertise needed to adopt these cutting-edge systems in their own infrastructure management workflows.

• Introduces a hybrid approach to transition from the unsupervised to the supervised SHM framework, where numerical models are used to cover scenarios that cannot be observed on existing structures
• Addresses new developments in sensing technology, facilitating more efficient maintenance and enabling the early identification of potential failures
• Explores the role of SHM in supporting climate change adaptation for bridges
• Lays the foundations for applying transfer learning in damage identification
• Compiles practical examples to provide a more comprehensive understanding of the statistical pattern recognition paradigm