Physics-informed neural networks used for Structural Health Monitoring in civil infrastructures

State of art and current challenges
DOI
URL
URI
Conference
35th European Safety and Reliability Conference and the 33rd Society for Risk Analysis Europe Conference, ESREL SRA-E 2025 ; Stavanger, Norway ; June 15–19, 2025
Publisher
Research Publishing
Book title
Stavanger ESREL SRA-E 2025 proceedings
ISBN
First page
1452
Last page
1459
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Structural Health Monitoring
Physics enhanced machine learning
Civil infrastructures
Physics informed neural networks
Graphical neural network
Transformers
Neural operators
Abstract
Structural Health Monitoring (SHM) is a fundamental task in the life-cycle assessment and management of civil infrastructures, specifically dams, locks, bridges, and roads. It aids in cost reduction, facilitates the early detection of degradation processes, damages, and structural deficiencies, ensures timely maintenance, and provides early risk warnings. SHM is directly related to the concept of Digital Twin, which is usually defined as a virtual replica of the physical asset. On the one hand, SHM provides the data for the implementation of digital twins, while on the other hand, digital twins can improve the effectiveness of SHM and support data analysis. Together, they represent a powerful combination for managing and maintaining critical infrastructure. A hybrid approach has become increasingly established in recent years, which comprises a combination of physics-based models and data-driven techniques. This approach mitigates the constraints of both models to align the digital twin’s behavior more closely with that of the corresponding physical asset. This study explores the hybrid modeling framework known as physics-enhanced machine learning for forecasting potential structural damage. Among the various hybrid modeling approaches, we focus on physics-informed neural networks (PINNs) and their applications in SHM of civil infrastructures. This study provides a comprehensive classification of research employing the PINNs architecture and critically evaluates its associated limitations. Additionally, we explore advanced deep learning architectures that can integrate PINNs within their computational frameworks to enhance SHM performance by addressing its limitations. This work is a foundational reference for understanding state-of-the-art advancements in PINNs for SHMapplications.
Version
Published version
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