{"title":"嵌入式应变传感器在预制隧道管片中的机器人部署","authors":"Tresor Tshimbombo;Marcus Perry;Hamish Dow;Jack McAlorum;Chris Hoy;Chrysoula Litina","doi":"10.1109/JSEN.2025.3559405","DOIUrl":null,"url":null,"abstract":"This article demonstrates the robotic deployment of sensor nodes into precast concrete tunnel segments during manufacturing. Magnetic embeddable sensor nodes based on vibrating wire strain gauges (VWSGs) were deployed on a steel precast segment mold 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 (FEM). Strain transfers across a population of ten segments were found to be <inline-formula> <tex-math>$0.93~\\pm ~0.012$ </tex-math></inline-formula> in the longitudinal direction, and <inline-formula> <tex-math>$0.567~\\pm ~0.011$ </tex-math></inline-formula> 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 article 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 (SHM) practitioners.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 11","pages":"18891-18900"},"PeriodicalIF":4.3000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robotic Deployment of Embedded Strain Sensors in Precast Tunnel Segments\",\"authors\":\"Tresor Tshimbombo;Marcus Perry;Hamish Dow;Jack McAlorum;Chris Hoy;Chrysoula Litina\",\"doi\":\"10.1109/JSEN.2025.3559405\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article demonstrates the robotic deployment of sensor nodes into precast concrete tunnel segments during manufacturing. Magnetic embeddable sensor nodes based on vibrating wire strain gauges (VWSGs) were deployed on a steel precast segment mold 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 (FEM). Strain transfers across a population of ten segments were found to be <inline-formula> <tex-math>$0.93~\\\\pm ~0.012$ </tex-math></inline-formula> in the longitudinal direction, and <inline-formula> <tex-math>$0.567~\\\\pm ~0.011$ </tex-math></inline-formula> 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 article 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 (SHM) practitioners.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"25 11\",\"pages\":\"18891-18900\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10965913/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10965913/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Robotic Deployment of Embedded Strain Sensors in Precast Tunnel Segments
This article demonstrates the robotic deployment of sensor nodes into precast concrete tunnel segments during manufacturing. Magnetic embeddable sensor nodes based on vibrating wire strain gauges (VWSGs) were deployed on a steel precast segment mold 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 (FEM). Strain transfers across a population of ten segments were found to be $0.93~\pm ~0.012$ in the longitudinal direction, and $0.567~\pm ~0.011$ 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 article 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 (SHM) practitioners.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
-Sensor Applications
-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice