A Framework for Optimal Sensor Placement to Support Structural Health Monitoring
Offshore or drydock inspection performed by trained surveyors is required within the integrity management of an in-service marine structure to ensure safety and fitness for purpose. However, these physical inspection activities can lead to a considerable increase in lifecycle cost and significant do...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/10/12/1819 |
| _version_ | 1797456907527520256 |
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| author | Shen Li Andrea Coraddu Feargal Brennan |
| author_facet | Shen Li Andrea Coraddu Feargal Brennan |
| author_sort | Shen Li |
| collection | DOAJ |
| description | Offshore or drydock inspection performed by trained surveyors is required within the integrity management of an in-service marine structure to ensure safety and fitness for purpose. However, these physical inspection activities can lead to a considerable increase in lifecycle cost and significant downtime, and they can impose hazards for the surveyors. To this end, the use of a structural health monitoring (SHM) system could be an effective resolution. One of the key performance indicators of an SHM system is its ability to predict the structural response of unmonitored locations by using monitored data, i.e., an inverse prediction problem. This is highly relevant in practical engineering, since monitoring can only be performed at limited and discrete locations, and it is likely that structurally critical areas are inaccessible for the installation of sensors. An accurate inverse prediction can be achieved, ideally, via a dense sensor network such that more data can be provided. However, this is usually economically unfeasible due to budget limits. Hence, to improve the monitoring performance of an SHM system, an optimal sensor placement should be developed. This paper introduces a framework for optimising the sensor placement scheme to support SHM. The framework is demonstrated with an illustrative example to optimise the sensor placement of a cantilever steel plate. The inverse prediction problem is addressed by using a radial basis function approach, and the optimisation is carried out by means of an evolutionary algorithm. The results obtained from the demonstration support the proposal. |
| first_indexed | 2024-03-09T16:14:36Z |
| format | Article |
| id | doaj.art-21c81932834647f8b78c52fb34f96c0e |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T16:14:36Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-21c81932834647f8b78c52fb34f96c0e2023-11-24T15:54:50ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-11-011012181910.3390/jmse10121819A Framework for Optimal Sensor Placement to Support Structural Health MonitoringShen Li0Andrea Coraddu1Feargal Brennan2Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G1 1XQ, UKFaculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, 2600 AA Delft, The NetherlandsDepartment of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G1 1XQ, UKOffshore or drydock inspection performed by trained surveyors is required within the integrity management of an in-service marine structure to ensure safety and fitness for purpose. However, these physical inspection activities can lead to a considerable increase in lifecycle cost and significant downtime, and they can impose hazards for the surveyors. To this end, the use of a structural health monitoring (SHM) system could be an effective resolution. One of the key performance indicators of an SHM system is its ability to predict the structural response of unmonitored locations by using monitored data, i.e., an inverse prediction problem. This is highly relevant in practical engineering, since monitoring can only be performed at limited and discrete locations, and it is likely that structurally critical areas are inaccessible for the installation of sensors. An accurate inverse prediction can be achieved, ideally, via a dense sensor network such that more data can be provided. However, this is usually economically unfeasible due to budget limits. Hence, to improve the monitoring performance of an SHM system, an optimal sensor placement should be developed. This paper introduces a framework for optimising the sensor placement scheme to support SHM. The framework is demonstrated with an illustrative example to optimise the sensor placement of a cantilever steel plate. The inverse prediction problem is addressed by using a radial basis function approach, and the optimisation is carried out by means of an evolutionary algorithm. The results obtained from the demonstration support the proposal.https://www.mdpi.com/2077-1312/10/12/1819structural health monitoringoptimisationstructural integrityevolutionary algorithmstress concentration |
| spellingShingle | Shen Li Andrea Coraddu Feargal Brennan A Framework for Optimal Sensor Placement to Support Structural Health Monitoring Journal of Marine Science and Engineering structural health monitoring optimisation structural integrity evolutionary algorithm stress concentration |
| title | A Framework for Optimal Sensor Placement to Support Structural Health Monitoring |
| title_full | A Framework for Optimal Sensor Placement to Support Structural Health Monitoring |
| title_fullStr | A Framework for Optimal Sensor Placement to Support Structural Health Monitoring |
| title_full_unstemmed | A Framework for Optimal Sensor Placement to Support Structural Health Monitoring |
| title_short | A Framework for Optimal Sensor Placement to Support Structural Health Monitoring |
| title_sort | framework for optimal sensor placement to support structural health monitoring |
| topic | structural health monitoring optimisation structural integrity evolutionary algorithm stress concentration |
| url | https://www.mdpi.com/2077-1312/10/12/1819 |
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