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Infrastructures
Special Issues
Bridge Modeling, Monitoring, Management and Beyond
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Bridge Modeling, Monitoring, Management and Beyond

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: 30 April 2025 | Viewed by 6950

Special Issue Editor

Dr. Ekin Ozer

E-Mail Website
Guest Editor
School of Civil Engineering, University College Dublin, D04V1W8 Dublin, Ireland
Interests: bridge engineering; structural health monitoring; system identification; structural dynamics; earthquake engineering; sensor technologies; machine learning; decision analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rise of next-generation sensing, learning, and analytics frameworks and platforms, we observe a significant step towards data-enabled automation in managing transportation infrastructure assets. The mechanical behavior of transportation infrastructure is subject to change over time, and digitized early warnings reflecting infrastructure conditions can dramatically increase the efficiency of stakeholders’ decisions without conventional labor-intensive efforts taking place. Concerning deterioration and failure, bridge structures are among the specific weak links of transportation networks; although they are not the sole degradable assets, and many other sources can trigger transportation service disruption.

This Special Issue is a broad attempt to bring state-of-the-art research on various aspects of bridge mechanics, from dynamic modeling and simulations to inverse dynamics incorporating diagnostics techniques such as structural health monitoring. The Special Issue is not only limited to the technical developments in modeling and monitoring frontiers but also covers the stakeholder-end post-diagnostics processes, i.e., consequence metrics, decision models, and potential maintenance actions conditioned to infrastructure diagnostics. Parallel advances in sensor technologies and hardware, finite element and surrogate modeling prospects, statistical learning approaches, hazard analysis and decision-making strategies are encouraged for representation in the Special Issue.

We hope this initiative will unify the existing research trends in bridge engineering for a complete understanding of data-enabled bridge risk mitigation and generate the essential knowledge for the 21st century’s digital transformation that can lead to more sustainable and resilient transportation infrastructure. Future recommender systems for bridge assets can be a vital part of this scheme, particularly addressed in this Special Issue. Topics included in this Special Issue include but are not limited to:

  • Bridge health monitoring
  • Bridge condition assessment/diagnostics
  • Emerging sensing techniques/sensor technologies for bridge monitoring
  • Bridge dynamics
  • Finite element modeling and model updating
  • Damage identification and classification
  • Predictive systems for prognostics/remaining useful life
  • Digital twins
  • Machine learning applications for bridge infrastructure
  • Hazard and risk analysis for bridges
  • Performance assessment of bridges
  • Bridge decision support systems
  • Bridge maintenance, retrofit, and management
  • Bridge networks

Yours sincerely,

Dr. Ekin Ozer
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Infrastructures is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bridge
  • sensor
  • dynamics
  • finite element
  • model
  • monitoring
  • reliability
  • risk
  • diagnostics
  • prognostics
  • machine learning
  • twins
  • damage
  • maintenance
  • decision

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Published Papers (5 papers)

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Research

28 pages, 8553 KiB  
Article
Recurrent Neural Network for Quantitative Time Series Predictions of Bridge Condition Ratings
by Adeyemi D. Sowemimo, Mi G. Chorzepa and Bjorn Birgisson
Infrastructures 2024, 9(12), 221; https://doi.org/10.3390/infrastructures9120221 - 6 Dec 2024
Viewed by 446
Abstract
Traditional forecasting models for bridge conditions, such as ARIMA and Markov chains, often fail to adequately capture nonlinear and dynamic relationships among critical variables like age, traffic patterns, and environmental factors, leading to suboptimal maintenance decisions, increased long-term maintenance costs, and heightened safety [...] Read more.
Traditional forecasting models for bridge conditions, such as ARIMA and Markov chains, often fail to adequately capture nonlinear and dynamic relationships among critical variables like age, traffic patterns, and environmental factors, leading to suboptimal maintenance decisions, increased long-term maintenance costs, and heightened safety risks. This study addresses these limitations by developing recurrent neural network (RNN) models utilizing Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures with a TimeDistributed output layer. This novel approach enables accurate forecasting of the Bridge Health Index (BHI) and condition ratings for key components—deck, superstructure, and substructure—while effectively modeling temporal dependencies. Applied to bridge data from Georgia, USA, the regression models (BHI) achieved R2 values exceeding 0.84, while the classification models (components condition ratings) demonstrated accuracy between 84.78% and 87.54%. By modeling complex temporal trends in bridge deterioration, our method processes time-dependent data from multiple bridges simultaneously, revealing intricate relationships that influence bridge performance within a state’s inventory. These results provide actionable insights for maintenance planning, optimized resource allocation, and reduced risks of unexpected failures. This research establishes a robust framework for bridge performance prediction, ensuring improved infrastructure safety and resilience amid aging assets and constrained maintenance budgets. Full article
(This article belongs to the Special Issue Bridge Modeling, Monitoring, Management and Beyond)
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15 pages, 9387 KiB  
Article
Extracting Bridge Modal Frequencies Using Stationary Versus Drive-By Modes of Smartphone Measurements
by Niall McSweeney, Ramin Ghiasi, Abdollah Malekjafarian and Ekin Ozer
Infrastructures 2024, 9(12), 218; https://doi.org/10.3390/infrastructures9120218 - 3 Dec 2024
Viewed by 661
Abstract
In this research, we harmonize the two mobility approaches, stationary and mobile measurements, within the same framework to generate comparison opportunities, particularly in terms of identified bridge modal frequencies. Vibration tests were conducted to determine the natural frequency of a pedestrian bridge located [...] Read more.
In this research, we harmonize the two mobility approaches, stationary and mobile measurements, within the same framework to generate comparison opportunities, particularly in terms of identified bridge modal frequencies. Vibration tests were conducted to determine the natural frequency of a pedestrian bridge located in University College Dublin using smartphones. Both stationary and mobile smartphone measurements were collected, a novel use of two levels of mobility. Stationary measurements involved leaving the smartphone on the bridge deck at different positions along the bridge for a period of time, and mobile measurements were carried out using an electric scooter to ride across the bridge with the smartphone attached to the scooter deck. Single-output identification results were then compared to visualize the differences at two mobility levels. The tests showed that it is possible to extract the first natural frequency of the bridge using both stationary and mobile smartphone measurement techniques, although operational uncertainties seemed to alter the latter one. A first natural frequency of 5.45 Hz from a reference data acquisition system confirmed the accuracy of stationary smartphone data. On the other hand, the mobile data require consideration of the driving frequency, a function of the speed of the test vehicle and length of the bridge. These results show that smartphone sensors can be regarded as an alternative to industrial accelerometers with certain barriers to account for the multi-modality of the mobile sensing and identification process. Full article
(This article belongs to the Special Issue Bridge Modeling, Monitoring, Management and Beyond)
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29 pages, 46768 KiB  
Article
Maintenance Challenges in Maritime Environments and the Impact on Urban Mobility: Machico Stayed Bridge
by Raul Alves, Sérgio Lousada, José Manuel Naranjo Gómez and José Cabezas
Infrastructures 2024, 9(10), 180; https://doi.org/10.3390/infrastructures9100180 - 8 Oct 2024
Viewed by 1115
Abstract
This article investigates the challenges of maintaining the Machico Cable-Stayed Bridge in a marine environment, focusing on its implications for urban mobility. The primary problem addressed is the impact of harsh marine conditions on the structural integrity of the bridge, which poses significant [...] Read more.
This article investigates the challenges of maintaining the Machico Cable-Stayed Bridge in a marine environment, focusing on its implications for urban mobility. The primary problem addressed is the impact of harsh marine conditions on the structural integrity of the bridge, which poses significant challenges for ongoing maintenance and safety. The research highlights unique aspects such as the effects of saltwater exposure on materials and the interplay between infrastructure and urban transit dynamics. By emphasizing these critical issues, this study aims to provide insights into effective maintenance strategies and contribute to the broader discourse on urban mobility in coastal regions. Full article
(This article belongs to the Special Issue Bridge Modeling, Monitoring, Management and Beyond)
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17 pages, 5686 KiB  
Article
A Comprehensive Study on Unsupervised Transfer Learning for Structural Health Monitoring of Bridges Using Joint Distribution Adaptation
by Laura Souza, Marcus Omori Yano, Samuel da Silva and Eloi Figueiredo
Infrastructures 2024, 9(8), 131; https://doi.org/10.3390/infrastructures9080131 - 8 Aug 2024
Viewed by 1359
Abstract
Bridges are crucial transportation infrastructures with significant socioeconomic impacts, necessitating continuous assessment to ensure safe operation. However, the vast number of bridges and the technical and financial challenges of maintaining permanent monitoring systems in every single bridge make the implementation of structural health [...] Read more.
Bridges are crucial transportation infrastructures with significant socioeconomic impacts, necessitating continuous assessment to ensure safe operation. However, the vast number of bridges and the technical and financial challenges of maintaining permanent monitoring systems in every single bridge make the implementation of structural health monitoring (SHM) difficult for authorities. Unsupervised transfer learning, which reuses experimental or numerical data from well-known bridges to detect damage on other bridges with limited monitoring response data, has emerged as a promising solution. This solution can reduce SHM costs while ensuring the safety of bridges with similar characteristics. This paper investigates the limitations, challenges, and opportunities of unsupervised transfer learning via domain adaptation across datasets from various prestressed concrete bridges under distinct operational and environmental conditions. A feature-based transfer learning approach is proposed, where the joint distribution adaptation method is used for domain adaptation. As the main advantage, this study leverages the generalization of SHM for damage detection in prestressed concrete bridges with limited long-term monitoring data. Full article
(This article belongs to the Special Issue Bridge Modeling, Monitoring, Management and Beyond)
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19 pages, 10856 KiB  
Article
Analyzing Wind Effects on Long-Span Bridges: A Viable Numerical Modelling Methodology Using OpenFOAM for Industrial Applications
by Yuxiang Zhang, Reamonn MacReamoinn, Philip Cardiff and Jennifer Keenahan
Infrastructures 2023, 8(9), 130; https://doi.org/10.3390/infrastructures8090130 - 26 Aug 2023
Cited by 3 | Viewed by 2185
Abstract
Aerodynamic performance is of critical importance to the design of long-span bridges. Computational fluid dynamics (CFD) modelling offers bridge designers an opportunity to investigate aerodynamic performance for long-span bridges during the design phase as well as during operation of the bridge. It offers [...] Read more.
Aerodynamic performance is of critical importance to the design of long-span bridges. Computational fluid dynamics (CFD) modelling offers bridge designers an opportunity to investigate aerodynamic performance for long-span bridges during the design phase as well as during operation of the bridge. It offers distinct advantages when compared with the current standard practice of wind tunnel testing, which can have several limitations. The proposed revisions to the Eurocodes offer CFD as a methodology for wind analysis of bridges. Practicing engineers have long sought a computationally affordable, viable, and robust framework for industrial applications of using CFD to examine wind effects on long-span bridges. To address this gap in the literature and guidance, this paper explicitly presents a framework and demonstrates a workflow of analyzing wind effects on long-span bridges using open-source software, namely FreeCAD, OpenFOAM, and ParaView. Example cases are presented, and detailed configurations and general guidance are discussed during each step. A summary is provided of the validation of this methodology with field data collected from the structural health monitoring (SHM) systems of two long-span bridges. Full article
(This article belongs to the Special Issue Bridge Modeling, Monitoring, Management and Beyond)
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