Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
2.7
Journals
Infrastructures
Special Issues
Advances in Structural Health Monitoring and Industry 5.0 Innovations for...
share announcement

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 1357

Special Issue Editors

Dr. Alejandro Jiménez Rios

E-Mail Website
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
Dr. Pier Francesco Giordano

E-Mail Website
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
Dr. Alberto Barontini

E-Mail Website
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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

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 490
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
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Industry 5.0 Innovations for Bridge Management and Conservation)
►▼ Show Figures

Figure 1

Review

Jump to: Research

25 pages, 1924 KiB  
Review
AI in Structural Health Monitoring for Infrastructure Maintenance and Safety
by Vagelis Plevris and George Papazafeiropoulos
Infrastructures 2024, 9(12), 225; https://doi.org/10.3390/infrastructures9120225 - 7 Dec 2024
Viewed by 492
Abstract
This study explores the growing influence of artificial intelligence (AI) on structural health monitoring (SHM), a critical aspect of infrastructure maintenance and safety. This study begins with a bibliometric analysis to identify current research trends, key contributing countries, and emerging topics in AI-integrated [...] Read more.
This study explores the growing influence of artificial intelligence (AI) on structural health monitoring (SHM), a critical aspect of infrastructure maintenance and safety. This study begins with a bibliometric analysis to identify current research trends, key contributing countries, and emerging topics in AI-integrated SHM. We examine seven core areas where AI significantly advances SHM capabilities: (1) data acquisition and sensor networks, highlighting improvements in sensor technology and data collection; (2) data processing and signal analysis, where AI techniques enhance feature extraction and noise reduction; (3) anomaly detection and damage identification using machine learning (ML) and deep learning (DL) for precise diagnostics; (4) predictive maintenance, using AI to optimize maintenance scheduling and prevent failures; (5) reliability and risk assessment, integrating diverse datasets for real-time risk analysis; (6) visual inspection and remote monitoring, showcasing the role of AI-powered drones and imaging systems; and (7) resilient and adaptive infrastructure, where AI enables systems to respond dynamically to changing conditions. This review also addresses the ethical considerations and societal impacts of AI in SHM, such as data privacy, equity, and transparency. We conclude by discussing future research directions and challenges, emphasizing the potential of AI to enhance the efficiency, safety, and sustainability of infrastructure systems. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Industry 5.0 Innovations for Bridge Management and Conservation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop