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Structural Health Monitoring of Bridges

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 2470

Special Issue Editors


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Guest Editor
School of Civil Engineering, Southeast University, Nanjing 211189, China
Interests: bridge aerodynamics; characterization of extreme winds; simulation of random winds; AI-powered structural wind engineering
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Guest Editor
Mechanics, Sound, & Vibration Laboratory, Department of Civil Engineering, College of Engineering, National Taiwan University, Taipei 10617, Taiwan
Interests: behavior of reinforced, prestressed concrete and steel structures; bridge engineering; engineering material; machine learning; method of finite elements; structural health assessment and monitoring
Special Issues, Collections and Topics in MDPI journals
Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK
Interests: wind engineering; wind structure interaction; long span bridges; monitoring; resilience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bridges play a vital role in connecting communities, facilitating transportation, and supporting economic growth. However, over time, bridges are subjected to various environmental factors, aging, and heavy loads, which can lead to structural deterioration and potential safety risks. To ensure the longevity and safety of these critical infrastructure assets, it is essential to implement effective monitoring and assessment systems that can detect early signs of damage, assess structural integrity, and enable timely maintenance and repair interventions.

The field of structural health monitoring (SHM) has emerged as a powerful tool for continuously monitoring the condition of bridges and providing valuable insights into their performance. SHM combines advanced sensing technologies, data analysis techniques, and predictive models to monitor and evaluate the structural behavior of bridges in real-time or near-real-time.

This Special Issue aims to bring together researchers, practitioners, and industry experts to share their latest advancements, methodologies, case studies, and innovative applications in the field of structural health monitoring of bridges. We invite contributions that address various aspects of bridge monitoring, including but not limited to sensor technologies, data acquisition and processing, signal analysis, damage detection algorithms, risk assessment, decision-making frameworks, and integration with smart infrastructure systems.

Topics of interest for this Special Issue include, but are not limited to:

  • Novel sensor technologies for bridge monitoring and assessment;
  • Wireless sensor networks and IoT-based monitoring and assessment systems;
  • Damage detection and localization algorithms;
  • Structural health assessment and remaining service life prediction;
  • Risk assessment and reliability analysis of bridges;
  • Case studies and real-world applications of bridge monitoring;
  • Long-term evolution of structural performances of bridges.

We encourage researchers from academia, industry professionals, and practitioners to submit their original research articles, review papers, or technical notes to contribute to this Special Issue.

Dr. Tianyou Tao
Dr. Marco Bonopera
Dr. John Owen
Guest Editors

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Published Papers (2 papers)

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Research

12 pages, 3085 KiB  
Article
Fatigue Crack Detection Based on Semantic Segmentation Using DeepLabV3+ for Steel Girder Bridges
by Xuejun Jia, Yuxiang Wang and Zhen Wang
Appl. Sci. 2024, 14(18), 8132; https://doi.org/10.3390/app14188132 - 10 Sep 2024
Abstract
Artificial intelligence technology is receiving more and more attention in structural health monitoring. Fatigue crack detection in steel box girders in long-span bridges is an important and challenging task. This paper presents a semantic segmentation network model for this task based on DeepLabv3+, [...] Read more.
Artificial intelligence technology is receiving more and more attention in structural health monitoring. Fatigue crack detection in steel box girders in long-span bridges is an important and challenging task. This paper presents a semantic segmentation network model for this task based on DeepLabv3+, ResNet50, and active learning. Specifically, the classification network ResNet50 is re-tuned using the crack image dataset. Secondly, with the re-tuned ResNet50 as the backbone network, a crack semantic segmentation network was constructed based on DeepLabv3+, which was trained with the assistance of active learning. Finally, optimization for the probability threshold of the pixel category was performed to improve the pixel-level detection accuracy. Tests show that, compared with the crack detection network based on conventional ResNet50, this model can improve MIoU from 0.6181 to 0.7241. Full article
(This article belongs to the Special Issue Structural Health Monitoring of Bridges)
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16 pages, 2793 KiB  
Article
Some Considerations about the Incorporation of Dynamic Parameters in the Structural Health Monitoring Systems of Bridges
by Juan-Antonio López-Aragón and Miguel-Ángel Astiz
Appl. Sci. 2024, 14(1), 33; https://doi.org/10.3390/app14010033 - 20 Dec 2023
Cited by 3 | Viewed by 1072
Abstract
Currently, there is a growing concern about the conservation and maintenance of infrastructure. Within this context, bridges deserve special attention, given their technical complexity and strategic nature. To this end, modern technology provides the opportunity to implement systems for structural health monitoring (SHM), [...] Read more.
Currently, there is a growing concern about the conservation and maintenance of infrastructure. Within this context, bridges deserve special attention, given their technical complexity and strategic nature. To this end, modern technology provides the opportunity to implement systems for structural health monitoring (SHM), a field in which great advances have been made in recent years. In this sense, one of the fastest-growing lines of work in Civil Engineering is the early detection of incidents because of changes in the dynamic behaviour of structures. Throughout this paper, some of the most notable considerations that the authors have been appreciating in the latest structures studied are summarized. These may be of interest for the possible incorporation of dynamic parameters in SHM systems that could be implemented in other structures in the future. With this purpose, a review of the different issues that must be studied within the dynamic analysis of a structure is carried out, such as the structural typology, the type of instrumentation, the recorded accelerations, the analysis of the natural frequencies, the study of the modal damping ratio and the set of thresholds; issues that are also accompanied by examples observed in two real monitored structures. Full article
(This article belongs to the Special Issue Structural Health Monitoring of Bridges)
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