Advances in Structural Health Monitoring and Industry 5.0 Innovations for Bridge Management and Conservation

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 487

Special Issue Editors


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Guest Editor
Department of Architecture & Civil Engineering, University of Bath, Bath, UK
Interests: civil engineering; structural engineering; bridge engineering; conservation of historic buildings and bridges; sustainability; resilience; human-centrism; digital twins; Industry 5.0

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Guest Editor
Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Milano, Italy
Interests: structural health monitoring; value of information; bridge management; risk assessment
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
ISISE, ARISE, Department of Civil Engineering, University of Minho, Guimarães, Portugal
Interests: structural health monitoring; damage identification; optimal sensor placement; vulnerability assessment; digital twins; conservation of historic buildings

Special Issue Information

Dear Colleagues

Industry 5.0 focuses on the integration of advanced technologies with human-centric, resilient, and sustainable approaches to enhance system performance and decision-making. This paradigm shift emphasizes the synergy between digital technologies, such as artificial intelligence and digital twins, and human expertise to create more intelligent, adaptable, and efficient systems.

Within the context of bridge management and conservation, this Special Issue investigates how Industry 5.0 is transforming Structural Health Monitoring (SHM), i.e., the process of assessing the condition of structures in real-time or at regular intervals. Specifically, we will explore how emerging technologies—such as data acquisition, automated processes, and digital information and analysis—are revolutionizing the ways in which bridges are monitored, assessed, and maintained. These innovations enable better damage identification, facilitating proactive maintenance strategies and enhancing the overall resilience and efficiency of bridge infrastructures.

Our goal is to showcase how the integration of digitalization and human-centric technologies addresses modern challenges in bridge management. We invite contributions that explore various facets of this transformation, including but not limited to the following:

  • Sensor placement optimization.
  • Automation of damage detection, localization, and quantification.
  • Data mining and data fusion strategies for bridge SHM applications.
  • Data-driven, model-based, and hybrid SHM novel strategies.
  • Remaining bridge service life prediction.
  • SHM-aided decision-making processes.
  • Smart sensors, AI-driven analytics, and digital twins.

Dr. Alejandro Jiménez Rios
Dr. Pier Francesco Giordano
Dr. Alberto Barontini
Guest Editors

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

  • structural health monitoring
  • Industry 5.0
  • bridge engineering
  • digital twins
  • artificial intelligence
  • smart materials
  • human–machine interaction
  • cybersecurity in infrastructure
  • sustainability
  • resilience
  • human-centrism

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Published Papers (1 paper)

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Research

17 pages, 5063 KiB  
Article
Enhancing Recovery of Structural Health Monitoring Data Using CNN Combined with GRU
by Nguyen Thi Cam Nhung, Hoang Nguyen Bui and Tran Quang Minh
Infrastructures 2024, 9(11), 205; https://doi.org/10.3390/infrastructures9110205 - 16 Nov 2024
Viewed by 271
Abstract
Structural health monitoring (SHM) plays a crucial role in ensuring the safety of infrastructure in general, especially critical infrastructure such as bridges. SHM systems allow the real-time monitoring of structural conditions and early detection of abnormalities. This enables managers to make accurate decisions [...] Read more.
Structural health monitoring (SHM) plays a crucial role in ensuring the safety of infrastructure in general, especially critical infrastructure such as bridges. SHM systems allow the real-time monitoring of structural conditions and early detection of abnormalities. This enables managers to make accurate decisions during the operation of the infrastructure. However, for various reasons, data from SHM systems may be interrupted or faulty, leading to serious consequences. This study proposes using a Convolutional Neural Network (CNN) combined with Gated Recurrent Units (GRUs) to recover lost data from accelerometer sensors in SHM systems. CNNs are adept at capturing spatial patterns in data, making them highly effective for recognizing localized features in sensor signals. At the same time, GRUs are designed to model sequential dependencies over time, making the combined architecture particularly suited for time-series data. A dataset collected from a real bridge structure will be used to validate the proposed method. Different cases of data loss are considered to demonstrate the feasibility and potential of the CNN-GRU approach. The results show that the CNN-GRU hybrid network effectively recovers data in both single-channel and multi-channel data loss scenarios. Full article
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