Sustainability Evaluation of Highway Sign Support by Field Testing and Finite Element Analysis
The primary objective of this study is to evaluate the sustainability of highway sign supports through field testing and finite element analysis. The study aims to develop a predictive maintenance model to evaluate the service life of these structures. Sign support systems are important structures i...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Yildiz Technical University
2024-12-01
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| Series: | Journal of Sustainable Construction Materials and Technologies |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/en/download/article-file/4467756 |
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| Summary: | The primary objective of this study is to evaluate the sustainability of highway sign supports through field testing and finite element analysis. The study aims to develop a predictive maintenance model to evaluate the service life of these structures. Sign support systems are important structures in the Connecticut Department of Transportation (CTDOT) bridge management system. Periodic sustainability inspections and maintenance activities are needed as a long-term, cost-effective maintenance strategy. The research involved non-destructive field testing of a cantilever-type highway sign support, followed by finite element modeling using Highway Sign Structures Engineering (HSE) by SAFI software. Data from accelerometers, strain gauges, and anemometers were collected and analyzed to validate the model. The experimental setup was done in collaboration with CTDOT. The data was collected and analyzed, and it was usedto verify the three-dimensional finite element (FE) model developed, which was used to test the structure's design capacity. The study found that the sign support structure experienced significant wind loading on a few occasions, with stress levels reaching about 20% of its elasticlimit. The finite element model accurately predicted structural behavior under design load conditions, demonstrating its potential for predictive maintenance applications. |
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| ISSN: | 2458-973X |