Piezoresistive Smart Bricks for Structural Health Monitoring of Masonry Arch Bridges: An Exploratory Numerical Study

Masonry arch bridges are critical assets in aging transportation networks, yet their Structural Health Monitoring (SHM) remains challenging. Smart bricks—piezoresistive sensing units compatible with masonry structures and capable of acting simultaneously as load-bearing components and strain sensors—offer a promising solution for embedding self-sensing capability directly within the masonry. While previous work by the authors has investigated their use in masonry walls, their application to arched structures remains unexplored. This gap is particularly significant given that arches, characterized by a predominantly compressive stress state, represent a natural context for smart-brick implementation. This study presents a numerical investigation assessing the potential of smart bricks for strain-based SHM of masonry arch bridges. A Finite Element (FE) model, derived from a validated experimental benchmark representative of typical Italian railway arch bridges, was used to virtually embed smart bricks at selected cross-sections along the arch. Damage progression was simulated through cyclic loading–unloading stages, enabling direct correlation between strain evolution and structural deterioration. Results demonstrate that smart bricks accurately capture damage-driven strain redistributions, closely mirroring both the sequence of damage formation and the associated collapse mechanism. These findings support the use of smart bricks for early detection of localized structural changes in masonry arches, providing a foundation for future experimental validation and real-world deployment of minimally invasive SHM systems.

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Publication Details

Journal
Infrastructures
Published
2026-04-22
DOI
https://doi.org/10.3390/infrastructures11050144
Primary Topic
Structural Health Monitoring Techniques
Type
article
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article

Piezoresistive Smart Bricks for Structural Health Monitoring of Masonry Arch Bridges: An Exploratory Numerical Study

Filippo Ubertini, Andrea Meoni, Michele Mattiacci, Alina Elena Eva et al.
Infrastructures
Structural Health Monitoring Techniques
article

Piezoresistive Smart Bricks for Structural Health Monitoring of Masonry Arch Bridges: An Exploratory Numerical Study

Filippo Ubertini, Andrea Meoni, Michele Mattiacci, Alina Elena Eva, Francesco Falini
article en

Abstract

Masonry arch bridges are critical assets in aging transportation networks, yet their Structural Health Monitoring (SHM) remains challenging. Smart bricks—piezoresistive sensing units compatible with masonry structures and capable of acting simultaneously as load-bearing components and strain sensors—offer a promising solution for embedding self-sensing capability directly within the masonry. While previous work by the authors has investigated their use in masonry walls, their application to arched structures remains unexplored. This gap is particularly significant given that arches, characterized by a predominantly compressive stress state, represent a natural context for smart-brick implementation. This study presents a numerical investigation assessing the potential of smart bricks for strain-based SHM of masonry arch bridges. A Finite Element (FE) model, derived from a validated experimental benchmark representative of typical Italian railway arch bridges, was used to virtually embed smart bricks at selected cross-sections along the arch. Damage progression was simulated through cyclic loading–unloading stages, enabling direct correlation between strain evolution and structural deterioration. Results demonstrate that smart bricks accurately capture damage-driven strain redistributions, closely mirroring both the sequence of damage formation and the associated collapse mechanism. These findings support the use of smart bricks for early detection of localized structural changes in masonry arches, providing a foundation for future experimental validation and real-world deployment of minimally invasive SHM systems.

InfrastructuresVol. 11(5)
University of Perugia (IT)
Sustainable cities and communities
Openalex Percentile: Top 12%
Structural Health Monitoring Techniques
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