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Elsevier

Structural Health Monitoring of Bridges

A Pattern Recognition Paradigm

  • 1st Edition - November 2, 2026
  • Latest edition
  • Authors: Elói Figueiredo, Ionuţ Dragoş Moldovan
  • Language: English

Structural Health Monitoring of Bridges: A Pattern Recognition Paradigm proposes an innovative approach for infrastructure assessment, focusing on statistical pattern recogn… Read more

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Description

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.

Key features

  • 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

Readership

Postgraduate students, researchers, and academics in civil and structural engineering; earthquake engineering and infrastructure resilience; sustainable built environments’ design, operations, and management; transportation engineering; Engineering practitioners, R&D professionals, designers and design consultants, performance and risk assessors, constructors, facility managers, and other decision-making stakeholders in the AECO industries, specifically those responsible for the safety and service life of civil infrastructure; companies that develop sensors, network systems, and tools for data transmission and analysis, particularly those tailored for use in SHM applications for bridges

Table of contents

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

Product details

  • Edition: 1
  • Latest edition
  • Published: November 2, 2026
  • Language: English

About the authors

EF

Elói Figueiredo

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.

Affiliations and expertise
Full Professor, Faculty of Engineering, Universidade Lusófona, Lisbon, Portugal

IM

Ionuţ Dragoş Moldovan

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. Ionuţ 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).
Affiliations and expertise
Associate Professor, Faculty of Engineering, Universidade Lusófona, Lisbon, Portugal