Structural assessment based on vibration measurement test combined with an artificial neural network for the steel truss bridge
Article
Tran, M., Sousa, H., Ngo, T., Nguyen, B., Nguyen, Q., Nguyen, H., Baron, E., Matos, J. and Dang, S. 2023. Structural assessment based on vibration measurement test combined with an artificial neural network for the steel truss bridge. Applied Sciences. 13 (13), p. 7484. https://doi.org/10.3390/app13137484
Type | Article |
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Title | Structural assessment based on vibration measurement test combined with an artificial neural network for the steel truss bridge |
Authors | Tran, M., Sousa, H., Ngo, T., Nguyen, B., Nguyen, Q., Nguyen, H., Baron, E., Matos, J. and Dang, S. |
Abstract | Damage assessment is one of the most crucial issues for bridge engineers during the operational and maintenance phase, especially for existing steel bridges. Among several methodologies, the vibration measurement test is a typical approach, in which the natural frequency variation of the structure is monitored to detect the existence of damage. However, locating and quantifying the damage is still a big challenge for this method, due to the required human resources and logistics involved. In this regard, an artificial intelligence (AI)-based approach seems to be a potential way of overcoming such obstacles. This study deployed a comprehensive campaign to determine all the dynamic parameters of a predamaged steel truss bridge structure. Based on the results for mode shape, natural frequency, and damping ratio, a finite element model (FEM) was created and updated. The artificial intelligence network’s input data from the damage cases were then analysed and evaluated. The trained artificial neural network model was curated and evaluated to confirm the approach’s feasibility. During the actual operational stage of the steel truss bridge, this damage assessment system showed good performance, in terms of monitoring the structural behaviour of the bridge under some unexpected accidents. |
Keywords | ANN; FEM; damage assessment; structural health monitoring; steel truss bridge |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Creativity, Culture & Enterprise |
Publisher | MDPI |
Journal | Applied Sciences |
ISSN | |
Electronic | 2076-3417 |
Publication dates | |
Online | 25 Jun 2023 |
25 Jun 2023 | |
Publication process dates | |
Submitted | 30 May 2023 |
Accepted | 18 Jun 2023 |
Deposited | 13 Jan 2025 |
Output status | Published |
Publisher's version | License File Access Level Open |
Copyright Statement | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Digital Object Identifier (DOI) | https://doi.org/10.3390/app13137484 |
https://repository.mdx.ac.uk/item/16q687
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