Finite element model updating for the dynamic response of continuous deck railway viaducts leading to efficient digital twins Article Swipe
Nicola Tarque
,
José María Goicolea Ruigómez
,
Rocío G. Cuevas
,
José Luis Martínez
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2647/10/102009
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2647/10/102009
Bridges and viaducts are key infrastructure elements within high speed railway lines, for which dynamic actions due to traffic are one of the main actions for design of new structures or evaluation of existing ones. This paper describes techniques based on experimental measurements and numerical finite element models leading to efficient digital twins, to be used for predictive maintenance, within the framework of an innovative prototype for a continuous predictive maintenance system installed in the La Marota 381 m long viaduct, in the high-speed line between Córdoba and Málaga in Spain. This system includes both model-based methods as well as data-based methods for maintenance decisions. Here we focus on the work for model-based acceleration performance, comparing in real time measurements from accelerometer sensors and predictions from a digital twin. The digital twin is an autonomous structural modal analysis software derived from a finite element model previously calibrated with Operational Modal Analysis experimental data. Results obtained from 3 representative high-speed trains are shown and compared in terms of root-mean-square (RMS) and moving window RMS of accelerations between the measured and numerical predictions. These allow to establish criteria and thresholds for predictive maintenance alerts.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2647/10/102009
- OA Status
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- Cited By
- 2
- References
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4400129663Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/2647/10/102009Digital Object Identifier
- Title
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Finite element model updating for the dynamic response of continuous deck railway viaducts leading to efficient digital twinsWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
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2024-06-01Full publication date if available
- Authors
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Nicola Tarque, José María Goicolea Ruigómez, Rocío G. Cuevas, José Luis MartínezList of authors in order
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https://doi.org/10.1088/1742-6596/2647/10/102009Publisher landing page
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diamondOpen access status per OpenAlex
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https://doi.org/10.1088/1742-6596/2647/10/102009Direct OA link when available
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Deck, Finite element method, Computer science, Structural engineering, Element (criminal law), Engineering, Political science, LawTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.describes | 38 |
| abstract_inverted_index.efficient | 51 |
| abstract_inverted_index.establish | 185 |
| abstract_inverted_index.framework | 62 |
| abstract_inverted_index.installed | 73 |
| abstract_inverted_index.numerical | 45, 180 |
| abstract_inverted_index.prototype | 66 |
| abstract_inverted_index.autonomous | 135 |
| abstract_inverted_index.calibrated | 147 |
| abstract_inverted_index.continuous | 69 |
| abstract_inverted_index.data-based | 101 |
| abstract_inverted_index.decisions. | 105 |
| abstract_inverted_index.evaluation | 32 |
| abstract_inverted_index.high-speed | 84, 159 |
| abstract_inverted_index.innovative | 65 |
| abstract_inverted_index.predictive | 58, 70, 190 |
| abstract_inverted_index.previously | 146 |
| abstract_inverted_index.structural | 136 |
| abstract_inverted_index.structures | 30 |
| abstract_inverted_index.techniques | 39 |
| abstract_inverted_index.thresholds | 188 |
| abstract_inverted_index.Operational | 149 |
| abstract_inverted_index.maintenance | 71, 104, 191 |
| abstract_inverted_index.model-based | 96, 113 |
| abstract_inverted_index.predictions | 125 |
| abstract_inverted_index.acceleration | 114 |
| abstract_inverted_index.experimental | 42, 152 |
| abstract_inverted_index.maintenance, | 59 |
| abstract_inverted_index.measurements | 43, 120 |
| abstract_inverted_index.performance, | 115 |
| abstract_inverted_index.predictions. | 181 |
| abstract_inverted_index.accelerations | 175 |
| abstract_inverted_index.accelerometer | 122 |
| abstract_inverted_index.infrastructure | 6 |
| abstract_inverted_index.representative | 158 |
| abstract_inverted_index.root-mean-square | 168 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5078949923, https://openalex.org/A5113269804 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I88060688 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.67420568 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |