Strain-based damage detection using nonlinear cointegration theory: Application to a masonry building model using smart bricks

Michele Mattiacci, Andrea Meoni, Antonella D’Alessandro, Branko Glisic, Filippo Ubertini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Structural health monitoring of masonry structures, relying on strain-based strategies, requires capable techniques for distinguishing damage-induced strain variations from those caused by environmental fluctuations. This study presents a novel SHM approach developed within the framework of nonlinear cointegration, specifically designed to process strain measurements and eliminate the influence of external environmental factors such as temperature and relative humidity. The strategy leverages neural networks to model the complex nonlinear relationship between environmental variables and the acquired strain time series, enabling the extraction of damage-sensitive features that remain unaffected by environmental conditions. By ensuring that strain measurements are corrected for environmental influences, the proposed method enhances the reliability of damage detection, allowing for a more accurate assessment of structural integrity. The effectiveness of this approach is evaluated through its application to a full-scale masonry building mock-up subjected to controlled damage scenarios under naturally varying environmental conditions and equipped with innovative brick-like strain sensors. Results demonstrate that the cointegration-based strategy effectively isolates damage-induced strain changes while suppressing misleading variations due to environmental fluctuations. The findings confirm that nonlinear cointegration, in combination with neural networks and smart bricks, provides a robust framework for continuous strain monitoring in masonry structures. This methodology offers significant advancements in structural health monitoring by improving the sensitivity and robustness of strain-based damage detection strategies, paving the way for more reliable monitoring solutions in real-world applications. The study highlights the potential of data-driven techniques to enhance structural assessment and long-term maintenance strategies for heritage as well as modern masonry structures.

Original languageEnglish (US)
Title of host publicationSensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2025
EditorsMaria Pina Limongelli, Ching Tai Ng, Didem Ozevin
PublisherSPIE
ISBN (Electronic)9781510686564
DOIs
StatePublished - 2025
EventSensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2025 - Vancouver, Canada
Duration: Mar 17 2025Mar 20 2025

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume13435
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2025
Country/TerritoryCanada
CityVancouver
Period3/17/253/20/25

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • Damage detection
  • Machine learning
  • Neural Networks
  • Nonlinear cointegration
  • Smart materials
  • Static monitoring
  • Structural health monitoring

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