Structural Health Monitoring of Bridges

Structural Health Monitoring of Bridges: A Pattern Recognition Paradigm proposes an innovative approach for infrastructure assessment, focusing on statistical pattern recognition (SPR) and advanced machine learning techniques. The book introduces 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. Special attention is given to data processing techniques that allow for the detection of damage patterns without relying on subjective human or destructive appraisal.

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 statistical pattern recognition paradigms