Autorentext

PhD in Civil Engineering (2010) and Full Professor at Universidade Lusófona (Portugal). Throughout his academic career, Elói has taught courses in the field of static and dynamic structural analysis, seismic engineering, and design of reinforced and prestressed concrete structures. His research has mainly focused on structural health monitoring (SHM) and management of bridges, particularly on damage identification based on machine learning techniques and finite element modeling. He is an Associate Editor of Structural Health Monitoring (SAGE) and a prolific author of books, book chapters, peer-reviewed journal articles, and conference proceedings, all of which also reflect his collaborative stance with experts from across the globe. He has recently been awarded an EEA grant to study the impact of climate change on the structural health of bridges (ClimaBridge Project) and is the leader of the Civil Research Group at Universidade Lusófona to promote sustainable and resilient infrastructure.

PhD in Civil Engineering (2008) from the University of Lisbon, Associate Professor at Universidade Lusófona, and Research Associate at the CERIS Research Centre (University of Lisbon). His research interests include the development of non-conventional (hybrid-Trefftz) finite elements with applications to geomechanics, structural mechanics, heat transfer and acoustics, and structural health monitoring of civil engineering structures. Ionu ****is the Principal Investigator of the research project CEN-DynaGeo (Coupled Experimental and Numerical Approaches Toward Reliable Dynamic Characterization of Multi-phase Geomaterials”), funded by the Science and Technology Foundation of Portugal, and is participating simultaneously in four other research projects with national and European exposure. He is a prolific author, holds a patent for an innovative procedure for the dynamic characterization of soils using hybrid-Trefftz finite elements, and is the initiator and lead developer of FreeHyTE (e.g., the first public, open-source, and user-friendly computational platform using hybrid-Trefftz finite elements).

Klappentext
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.

Inhalt

1. Introduction
   
   2. Bridge management
   3. Case studies: structural description and data sets
   4. An overview of structural health monitoring
   5. Statistical pattern recognition
   6. Probabilistic numerical models for hybrid databases
   7. Unsupervised learning strategy
   8. Supervised learning strategy
   9. Transfer learning
   10. The role of SHM for climate change adaptation
   11. Limitation, challenges, and future trends