Wireless Concrete Strength Monitoring of Wind Turbine Foundations
Wind turbine foundations are typically cast in place, leaving the concrete to mature under environmental conditions that vary in time and space. As a result, there is uncertainty around the concrete’s initial performance, and this can encourage both costly over-design and inaccurate prognoses of str...
| Published in: | Sensors |
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| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-12-01
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| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/12/2928 |
| _version_ | 1852769110986850304 |
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| author | Marcus Perry Grzegorz Fusiek Pawel Niewczas Tim Rubert Jack McAlorum |
| author_facet | Marcus Perry Grzegorz Fusiek Pawel Niewczas Tim Rubert Jack McAlorum |
| author_sort | Marcus Perry |
| collection | DOAJ |
| container_title | Sensors |
| description | Wind turbine foundations are typically cast in place, leaving the concrete to mature under environmental conditions that vary in time and space. As a result, there is uncertainty around the concrete’s initial performance, and this can encourage both costly over-design and inaccurate prognoses of structural health. Here, we demonstrate the field application of a dense, wireless thermocouple network to monitor the strength development of an onshore, reinforced-concrete wind turbine foundation. Up-to-date methods in fly ash concrete strength and maturity modelling are used to estimate the distribution and evolution of foundation strength over 29 days of curing. Strength estimates are verified by core samples, extracted from the foundation base. In addition, an artificial neural network, trained using temperature data, is exploited to demonstrate that distributed concrete strengths can be estimated for foundations using only sparse thermocouple data. Our techniques provide a practical alternative to computational models, and could assist site operators in making more informed decisions about foundation design, construction, operation and maintenance. |
| format | Article |
| id | doaj-art-41aff8617cd54dfbb38ea7e9bc85f229 |
| institution | Directory of Open Access Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| spelling | doaj-art-41aff8617cd54dfbb38ea7e9bc85f2292025-08-19T20:52:35ZengMDPI AGSensors1424-82202017-12-011712292810.3390/s17122928s17122928Wireless Concrete Strength Monitoring of Wind Turbine FoundationsMarcus Perry0Grzegorz Fusiek1Pawel Niewczas2Tim Rubert3Jack McAlorum4Department of Civil & Environmental Engineering, University of Strathclyde, Glasgow G1 1XJ, UKDepartment of Electronic & Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UKDepartment of Electronic & Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UKDepartment of Electronic & Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UKDepartment of Electronic & Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UKWind turbine foundations are typically cast in place, leaving the concrete to mature under environmental conditions that vary in time and space. As a result, there is uncertainty around the concrete’s initial performance, and this can encourage both costly over-design and inaccurate prognoses of structural health. Here, we demonstrate the field application of a dense, wireless thermocouple network to monitor the strength development of an onshore, reinforced-concrete wind turbine foundation. Up-to-date methods in fly ash concrete strength and maturity modelling are used to estimate the distribution and evolution of foundation strength over 29 days of curing. Strength estimates are verified by core samples, extracted from the foundation base. In addition, an artificial neural network, trained using temperature data, is exploited to demonstrate that distributed concrete strengths can be estimated for foundations using only sparse thermocouple data. Our techniques provide a practical alternative to computational models, and could assist site operators in making more informed decisions about foundation design, construction, operation and maintenance.https://www.mdpi.com/1424-8220/17/12/2928concrete maturitywireless sensingneural networksstructural health monitoringfoundation design |
| spellingShingle | Marcus Perry Grzegorz Fusiek Pawel Niewczas Tim Rubert Jack McAlorum Wireless Concrete Strength Monitoring of Wind Turbine Foundations concrete maturity wireless sensing neural networks structural health monitoring foundation design |
| title | Wireless Concrete Strength Monitoring of Wind Turbine Foundations |
| title_full | Wireless Concrete Strength Monitoring of Wind Turbine Foundations |
| title_fullStr | Wireless Concrete Strength Monitoring of Wind Turbine Foundations |
| title_full_unstemmed | Wireless Concrete Strength Monitoring of Wind Turbine Foundations |
| title_short | Wireless Concrete Strength Monitoring of Wind Turbine Foundations |
| title_sort | wireless concrete strength monitoring of wind turbine foundations |
| topic | concrete maturity wireless sensing neural networks structural health monitoring foundation design |
| url | https://www.mdpi.com/1424-8220/17/12/2928 |
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