Robotic Deployment of Embedded Strain Sensors in Precast Tunnel Segments

The paper demonstrates the robotic deployment of sensor nodes into precast concrete tunnel segments during manufacturing. Magnetic embeddable sensor nodes based on vibrating wire strain gauges were deployed on a steel precast segment mould using a six-axis collaborative robot at the lab scale. Robotic sensor deployment proved to be significantly more accurate and consistent than manual sensor deployment methods. On average, positional and angular errors in sensor placement were reduced by 85% when using robotic deployment. The strain transfer coefficients for robotically embedded sensors were evaluated using mechanical bending tests and a finite element model. Strain transfers across a population of 10 segments were found to be 0.93 ± 0.012 in the longitudinal direction, and 0.567 ± 0.011 in the transversal direction. The repeatability of strain measurements within these segments was also confirmed, with low coefficient of variation values of 1% for longitudinal strains and 1.9% for transversal strains. The work presented in this paper underscores the measurement performance enhancements that can result from using robotics for sensor deployment in precast manufacturing environments. This could translate to a lower uncertainty and risk for civil asset managers and structural health monitoring practitioners.

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Publication Details

Journal
IEEE Sensors Journal
Published
2025-04-15
DOI
https://doi.org/10.1109/jsen.2025.3559405
Primary Topic
Tunneling and Rock Mechanics
Type
article
Field-Weighted Citation Impact
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article

Robotic Deployment of Embedded Strain Sensors in Precast Tunnel Segments

Chris Hoy, Marcus Perry, Tresor Tshimbombo, Chrysoula Litina et al.
IEEE Sensors Journal
Tunneling and Rock Mechanics
article

Robotic Deployment of Embedded Strain Sensors in Precast Tunnel Segments

Chris Hoy, Marcus Perry, Tresor Tshimbombo, Chrysoula Litina, Hamish Dow, Jack McAlorum
article en

Abstract

The paper demonstrates the robotic deployment of sensor nodes into precast concrete tunnel segments during manufacturing. Magnetic embeddable sensor nodes based on vibrating wire strain gauges were deployed on a steel precast segment mould using a six-axis collaborative robot at the lab scale. Robotic sensor deployment proved to be significantly more accurate and consistent than manual sensor deployment methods. On average, positional and angular errors in sensor placement were reduced by 85% when using robotic deployment. The strain transfer coefficients for robotically embedded sensors were evaluated using mechanical bending tests and a finite element model. Strain transfers across a population of 10 segments were found to be 0.93 ± 0.012 in the longitudinal direction, and 0.567 ± 0.011 in the transversal direction. The repeatability of strain measurements within these segments was also confirmed, with low coefficient of variation values of 1% for longitudinal strains and 1.9% for transversal strains. The work presented in this paper underscores the measurement performance enhancements that can result from using robotics for sensor deployment in precast manufacturing environments. This could translate to a lower uncertainty and risk for civil asset managers and structural health monitoring practitioners.

IEEE Sensors JournalVol. 25(11)
University of Strathclyde (GB), Highways England (GB), Glasgow Life (GB), Cowi (Denmark) (DK)
Openalex Percentile: Top 92%
Tunneling and Rock Mechanics
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Robotic Deployment of Embedded Strain Sensors in Precast Tunnel Segments — Chris Hoy, Marcus Perry, et al. · IEEE Sensors Journal (2025) | TGRS Research Map | TGRS