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Infrastructures
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Advances in Structural Health Monitoring and Industry 5.0 Innovations for...
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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: 30 June 2026 | Viewed by 36698

Special Issue Editors

Dr. Alejandro Jiménez Rios

E-Mail Website
Guest Editor
Department of Architecture & Civil Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK
Interests: architecture; civil engineering; structural engineering; bridge engineering; building engineering; sustainability; resilience; human centrism; digital twins; built cultural heritage; conservation; masonry structures; earthen structures; regenerative design; nature-based solutions
Special Issues, Collections and Topics in MDPI journals
Dr. Pier Francesco Giordano

E-Mail Website
Guest Editor
Politecnico di Milano, Department of Architecture, Built Environment and Construction Engineering (ABC), Piazza Leonardo da Vinci, 32, 20133 Milano, Italy
Interests: structural health monitoring; value of information; InSAR; bridge management systems
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 250 words) can be sent to the Editorial Office for assessment.

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

Published Papers (7 papers)

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Research

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22 pages, 3535 KB  
Article
Bridge Health Monitoring and Assessment in Industry 5.0: Lessons Learned from Long-Term Real-Time Field Monitoring of Highway Bridges
by Prakash Bhandari, Shinae Jang, Song Han and Ramesh B. Malla
Infrastructures 2026, 11(2), 55; https://doi.org/10.3390/infrastructures11020055 - 7 Feb 2026
Viewed by 651
Abstract
The rapid aging of bridges has increased interest in real-time, data-driven monitoring for predictive maintenance and safety management; however, practical deployment on in-service bridges remains limited. This paper presents lessons learned from long-term field deployment of real-time bridge joint monitoring systems on three [...] Read more.
The rapid aging of bridges has increased interest in real-time, data-driven monitoring for predictive maintenance and safety management; however, practical deployment on in-service bridges remains limited. This paper presents lessons learned from long-term field deployment of real-time bridge joint monitoring systems on three in-service highway bridges and demonstrates how these insights can support the transition toward Industry 5.0. A unified framework is introduced to integrate key enabling technologies, including Internet of Things (IoT), digital twins, and artificial intelligence (AI), into a practical, human-centric monitoring architecture. Best practices for achieving durable, site-compliant, and cost-effective system design are summarized, with emphasis on sensor selection, wireless communication strategies, modular system development, and maintaining seamless operation. The development of a Docker-based analytics and visualization platform illustrates how interactive dashboards enhance human–machine collaboration and support informed decision-making. The role of advanced analytical tools, including digital twins, AI, and statistical modeling, in providing reliable structural assessments is highlighted, along with guidance on balancing cloud and edge computing for energy-efficient performance under constraints such as limited power, weather exposure, and site accessibility. Overall, the findings support the development of scalable, resilient, and human-centric real-time monitoring systems that advance data-driven decision-making and directly contribute to the realization of Industry 5.0 objectives in bridge health management. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Industry 5.0 Innovations for Bridge Management and Conservation)
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14 pages, 1515 KB  
Article
Zero-Shot Bridge Health Monitoring Using Cepstral Features and Streaming LSTM Networks
by Azin Mehrjoo, Kyle L. Hom, Homayoon Beigi and Raimondo Betti
Infrastructures 2025, 10(11), 292; https://doi.org/10.3390/infrastructures10110292 - 3 Nov 2025
Viewed by 949
Abstract
This paper presents a real-time, output-only structural health monitoring framework that integrates damage-sensitive cepstral features with a streaming Long Short-Term Memory (LSTM) network for automated damage detection. Acceleration time histories are segmented into overlapping windows, converted into cepstral coefficients, and processed sequentially by [...] Read more.
This paper presents a real-time, output-only structural health monitoring framework that integrates damage-sensitive cepstral features with a streaming Long Short-Term Memory (LSTM) network for automated damage detection. Acceleration time histories are segmented into overlapping windows, converted into cepstral coefficients, and processed sequentially by a stacked LSTM architecture with state carry-over. This design preserves temporal dependencies while enabling low-latency inference suitable for continuous monitoring. The framework was evaluated under a strict zero-shot setting on the full-scale Z24 Bridge benchmark, in which no training or calibration data from the bridge were used. Our results show that the proposed approach can reliably discriminate staged damage states and track their progression using only vibration measurements. By combining a well-established spectral feature representation with a streaming sequence model, the study demonstrates a practical pathway toward deployable, data-driven monitoring systems capable of operating without retraining on each individual asset. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Industry 5.0 Innovations for Bridge Management and Conservation)
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23 pages, 5554 KB  
Article
Innovative Forecasting: “A Transformer Architecture for Enhanced Bridge Condition Prediction”
by Manuel Fernando Flores Cuenca, Yavuz Yardim and Cengis Hasan
Infrastructures 2025, 10(10), 260; https://doi.org/10.3390/infrastructures10100260 - 29 Sep 2025
Viewed by 1654
Abstract
The preservation of bridge infrastructure has become increasingly critical as aging assets face accelerated deterioration due to climate change, environmental loading, and operational stressors. This issue is particularly pronounced in regions with limited maintenance budgets, where delayed interventions compound structural vulnerabilities. Although traditional [...] Read more.
The preservation of bridge infrastructure has become increasingly critical as aging assets face accelerated deterioration due to climate change, environmental loading, and operational stressors. This issue is particularly pronounced in regions with limited maintenance budgets, where delayed interventions compound structural vulnerabilities. Although traditional bridge inspections generate detailed condition ratings, these are often viewed as isolated snapshots rather than part of a continuous structural health timeline, limiting their predictive value. To overcome this, recent studies have employed various Artificial Intelligence (AI) models. However, these models are often restricted by fixed input sizes and specific report formats, making them less adaptable to the variability of real-world data. Thus, this study introduces a Transformer architecture inspired by Natural Language Processing (NLP), treating condition ratings, and other features as tokens within temporally ordered inspection “sentences” spanning 1993–2024. Due to the self-attention mechanism, the model effectively captures long-range dependencies in patterns, enhancing forecasting accuracy. Empirical results demonstrate 96.88% accuracy for short-term prediction and 86.97% across seven years, surpassing the performance of comparable time-series models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs). Ultimately, this approach enables a data-driven paradigm for structural health monitoring, enabling bridges to “speak” through inspection data and empowering engineers to “listen” with enhanced precision. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Industry 5.0 Innovations for Bridge Management and Conservation)
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17 pages, 5063 KB  
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
Cited by 8 | Viewed by 2543
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)
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Review

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30 pages, 2210 KB  
Review
Dynamic Response-Based Bridge Monitoring and Structural Assessment: A Structured Scoping Review and Evidence Inventory
by Muhammad Ziad Bacha, Mario Lucio Puppio, Marco Zucca and Mauro Sassu
Infrastructures 2026, 11(4), 134; https://doi.org/10.3390/infrastructures11040134 - 10 Apr 2026
Viewed by 264
Abstract
Dynamic response measurements support bridge monitoring and structural assessment because they are obtainable under operational loading and are sensitive to changes in stiffness, boundary conditions, and mass distribution. This article presents a structured scoping review of dynamic-response-based bridge monitoring and assessment. It covers [...] Read more.
Dynamic response measurements support bridge monitoring and structural assessment because they are obtainable under operational loading and are sensitive to changes in stiffness, boundary conditions, and mass distribution. This article presents a structured scoping review of dynamic-response-based bridge monitoring and assessment. It covers damage-sensitive indicators, stiffness/capacity proxy inference, interpretation under operational and extreme loading, sensing with acquisition (contact, and indirect/drive-by), and data processing, machine learning and digital-twin integration for decision support. Evidence was identified through targeted searches in Scopus and The Lens with duplicate resolution in Zotero. The cited studies are compiled into a traceable evidence inventory linked to method families and decision objectives. The synthesis shows that global modal properties enable change screening but are highly confounded by environmental/operational variability. Localization and state characterization typically require denser or higher-fidelity sensing and signal conditioning. Finally, capacity-related inference using calibrated conversion models or machine learning (ML) surrogates remains context-bounded and validation-dependent. This review provides an end-to-end pipeline, evidence-maturity rubric, and conservative failure-mode checks with escalation logic that tie SHM outputs to inspection and analysis rather than direct condition declarations for bridge owners. This review is intentionally scoped and does not claim PRISMA-style comprehensiveness. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Industry 5.0 Innovations for Bridge Management and Conservation)
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70 pages, 4598 KB  
Review
Maintenance Budget Allocation Models of Existing Bridge Structures: Systematic Literature and Scientometric Reviews of the Last Three Decades
by Eslam Mohammed Abdelkader, Abobakr Al-Sakkaf, Kyrillos Ebrahim and Moaaz Elkabalawy
Infrastructures 2025, 10(9), 252; https://doi.org/10.3390/infrastructures10090252 - 20 Sep 2025
Cited by 2 | Viewed by 2911
Abstract
Bridges play an increasingly indispensable role in endorsing the economic and social development of societies by linking highways and facilitating the mobility of people and goods. Concurrently, they are susceptible to high traffic volumes and an intricate service environment over their lifespans, resulting [...] Read more.
Bridges play an increasingly indispensable role in endorsing the economic and social development of societies by linking highways and facilitating the mobility of people and goods. Concurrently, they are susceptible to high traffic volumes and an intricate service environment over their lifespans, resulting in undergoing a progressive deterioration process. Hence, efficient measures of maintenance, repair, and rehabilitation planning are critical to boost the performance condition, safety, and structural integrity of bridges while evading less costly interventions. To this end, this research paper furnishes a mixed review method, comprising systematic literature and scientometric reviews, for the meticulous examination and analysis of the existing research work in relation with maintenance fund allocation models of bridges (BriMai_all). With that in mind, Scopus and Web of Science databases are harnessed collectively to retrieve peer-reviewed journal articles on the subject, culminating in 380 indexed journal articles over the study period (1990–2025). In this respect, VOSviewer and Bibliometrix R package are utilized to create a visualization network of the literature database, covering keyword co-occurrence analysis, country co-authorship analysis, institution co-authorship analysis, journal co-citation analysis, journal co-citation, core journal analysis, and temporal trends. Subsequently, a rigorous systematic literature review is rendered to synthesize the adopted tools and prominent trends of the relevant state of the art. Particularly, the conducted multi-dimensional review examines the six dominant methodical paradigms of bridge maintenance management: (1) multi-criteria decision making, (2) life cycle assessment, (3) digital twins, (4) inspection planning, (5) artificial intelligence, and (6) optimization. It can be argued that this research paper could assist asset managers with a practical guide and a protocol to plan maintenance expenditures and implement sustainable practices for bridges under deterioration. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Industry 5.0 Innovations for Bridge Management and Conservation)
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25 pages, 1924 KB  
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
Cited by 83 | Viewed by 25980
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)
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