Multi-Modal NDE Data Analysis for Bridge Assessment Using the BEAST Dataset and Temporal Graph Convolution Networks.
Saved in:
| Title: | Multi-Modal NDE Data Analysis for Bridge Assessment Using the BEAST Dataset and Temporal Graph Convolution Networks. |
|---|---|
| Authors: | Momtaz, Mozhgan1 (AUTHOR) mozhgan.momtaz.ctr@dot.gov, Azari, Hoda1 (AUTHOR) hoda.azari@dot.gov |
| Source: | Journal of Nondestructive Evaluation. Dec2025, Vol. 44 Issue 4, p1-27. 27p. |
| Subjects: | Bridge testing, Multisensor data fusion, Nondestructive testing, Repair & maintenance services, Prediction models, Structural health monitoring, Convolutional neural networks, Spatiotemporal processes |
| Abstract: | Preserving aging bridges, which are vital to transportation networks, presents notable difficulties due to factors like intense usage, structural wear, and restricted maintenance resources. This research examines the deployment of Nondestructive Evaluation (NDE) techniques to optimize bridge maintenance strategies and maintain structural soundness. Over the course of infrastructure lifespans, vast amounts of NDE data are accumulated, yet processing and interpreting this information proves challenging due to intricate spatial and temporal interdependencies. In this study, we approach the problem as one of graph-based prediction, introducing two advanced methodologies to address it. The primary approach utilizes a Temporal Graph Convolution Network (TGCN), harnessing spatio-temporal patterns for predictive modeling. The secondary approach, a multi-modal TGCN, integrates data fusion techniques to combine diverse data sources for improved predictive accuracy. We evaluate the performance of these approaches using NDE data collected at Rutgers' BEAST® facility that includes five NDE modalities and 14 consecutive time intervals for assessing bridge deck conditions, comparing the results against a baseline Spatio-Temporal Autoregressive (STAR) model. While the STAR model established foundational forecasts, the TGCN method achieved superior results by managing nonlinearities. The multi-modal TGCN further enhanced performance, demonstrating the advantages of leveraging data fusion to incorporate multiple data types within TGCN frameworks. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Nondestructive Evaluation is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 188712445 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Multi-Modal NDE Data Analysis for Bridge Assessment Using the BEAST Dataset and Temporal Graph Convolution Networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Momtaz%2C+Mozhgan%22">Momtaz, Mozhgan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mozhgan.momtaz.ctr@dot.gov</i><br /><searchLink fieldCode="AR" term="%22Azari%2C+Hoda%22">Azari, Hoda</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hoda.azari@dot.gov</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Nondestructive+Evaluation%22">Journal of Nondestructive Evaluation</searchLink>. Dec2025, Vol. 44 Issue 4, p1-27. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bridge+testing%22">Bridge testing</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Nondestructive+testing%22">Nondestructive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Repair+%26+maintenance+services%22">Repair & maintenance services</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+health+monitoring%22">Structural health monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Preserving aging bridges, which are vital to transportation networks, presents notable difficulties due to factors like intense usage, structural wear, and restricted maintenance resources. This research examines the deployment of Nondestructive Evaluation (NDE) techniques to optimize bridge maintenance strategies and maintain structural soundness. Over the course of infrastructure lifespans, vast amounts of NDE data are accumulated, yet processing and interpreting this information proves challenging due to intricate spatial and temporal interdependencies. In this study, we approach the problem as one of graph-based prediction, introducing two advanced methodologies to address it. The primary approach utilizes a Temporal Graph Convolution Network (TGCN), harnessing spatio-temporal patterns for predictive modeling. The secondary approach, a multi-modal TGCN, integrates data fusion techniques to combine diverse data sources for improved predictive accuracy. We evaluate the performance of these approaches using NDE data collected at Rutgers' BEAST® facility that includes five NDE modalities and 14 consecutive time intervals for assessing bridge deck conditions, comparing the results against a baseline Spatio-Temporal Autoregressive (STAR) model. While the STAR model established foundational forecasts, the TGCN method achieved superior results by managing nonlinearities. The multi-modal TGCN further enhanced performance, demonstrating the advantages of leveraging data fusion to incorporate multiple data types within TGCN frameworks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Nondestructive Evaluation is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=188712445 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10921-025-01267-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1 Subjects: – SubjectFull: Bridge testing Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Nondestructive testing Type: general – SubjectFull: Repair & maintenance services Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Structural health monitoring Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Spatiotemporal processes Type: general Titles: – TitleFull: Multi-Modal NDE Data Analysis for Bridge Assessment Using the BEAST Dataset and Temporal Graph Convolution Networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Momtaz, Mozhgan – PersonEntity: Name: NameFull: Azari, Hoda IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 01959298 Numbering: – Type: volume Value: 44 – Type: issue Value: 4 Titles: – TitleFull: Journal of Nondestructive Evaluation Type: main |
| ResultId | 1 |
