Advancing structural health monitoring in civil engineering with Greybox Modelling

Karimi, Saboor; Gündel, Max

doi:10.58286/29668

Advancing structural health monitoring in civil engineering with Greybox Modelling

A Review

Publication date

2024-07-01

Document type

Konferenzbeitrag

Author

Karimi, Saboor

Gündel, Max

Organisational unit

Stahlbau und Stahlwasserbau

DTEC.bw

DOI

10.58286/29668

URI

https://openhsu.ub.hsu-hh.de/handle/10.24405/22842

Conference

11th European Workshop on Structural Health Monitoring (EWSHM 2024) ; Potsdam, Germany ; June 10–13, 2024

Publisher

NDT.net

Series or journal

e-Journal of Nondestructive Testing

ISSN

1435-4934

Periodical volume

29

Periodical issue

7

Part of the university bibliography

✅

Language

English

Keyword

dtec.bw

Abstract

This review explores the application of Greybox Modelling, specifically Physics-Informed Machine Learning (PIML), in the context of Structural Health Monitoring (SHM) within civil engineering. SHM is vital for ensuring the safety and durability of infrastructure namely bridges. Greybox Modelling, merging physics-based principles with data-driven techniques, offers a functional approach for understanding complex structural behaviors. By leveraging existing knowledge of physical laws, this method extracts valuable insights from sensor data. The integration of Greybox Modelling takes SHM a step further. By applying machine learning algorithms with the physics that govern civil engineering structures, Greybox Modelling bridges the gap between data-driven models and physics-based models. This leads more accurate predictions and interpretable outcomes. This review discusses findings from literature, recent studies, that illustrate the effectiveness of Greybox Modelling, specifically PIML in various civil engineering applications. These case studies highlight how this approach can adapt to different structural designs and environmental conditions typically encountered in civil engineering. Moreover, they indicate its potential for damage detection and early warning systems, which enhance the safety and efficiency of infrastructure. The paper also addresses current challenges and outlining future directions in the field.

Description

This work is licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

Version

Published version

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