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Advances in Performance Monitoring and Anomaly Detection across Structures and...
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Advances in Performance Monitoring and Anomaly Detection across Structures and Industrial Processes

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 2139

Special Issue Editors

Dr. Alessandro De Luca

E-Mail Website
Guest Editor
Department of Engineering, University of Campania "Luigi Vanvitelli", 81031 Aversa, Italy
Interests: structural health monitoring; finite element analysis; structural behavior; mechanical design; crashworthiness
Special Issues, Collections and Topics in MDPI journals
Dr. Donato Perfetto

E-Mail Website
Guest Editor
Department of Engineering, University of Campania “Luigi Vanvitelli”, 81031 Aversa, Italy
Interests: structural health monitoring; FRP composite materials; finite element analysis (FEA); crashworthiness; structural behavior
Special Issues, Collections and Topics in MDPI journals
Dr. Raffaele Ciardiello

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy
Interests: mechanical properties of composite materials and structures; multifunctional composite materials; structural health monitoring; structural design optimisation; adhesive joining methodology; reversible adhesive; adhesive joints for composite materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the evolving landscape of engineering and industrial applications, the integration of non-destructive evaluation (NDE) methods and structural health monitoring (SHM) systems has become paramount. These methodologies enhance the sustainability and efficiency of monitoring, maintenance, and repair operations for a wide array of structures and industrial processes. This Special Issue aims to showcase the latest advancements in performance monitoring, anomaly detection, and the assessment of structures and processes via innovative NDE and SHM strategies.

You are invited to propose contributions that push the boundaries of current practices in NDE and SHM, offering novel insights into their application across various engineering fields. Submissions may encompass theoretical, analytical, numerical, or experimental investigations, including comprehensive review articles.

The potential topics of this Special Issue include, but are not limited to, the following:

  • NDE methods;
  • SHM systems;
  • Numerical modelling to support the design of NDE methods and SHM systems;
  • SHM and NDE practices for maintenance management and repair operations;
  • Anomaly diagnosis and prognosis;
  • Measurements and processing algorithms for the identification and characterization of anomalies;
  • Machine learning algorithms and novel approaches for unknown scenarios prediction and residual life estimation;
  • Numerical and experimental investigations of damage detection and classification;
  • Novel signal and image processing algorithms for damage diagnosis and prognosis;
  • Structural behavior of damaged structure;
  • Assessment of load-carrying capacity of pristine and damaged structures;
  • Smart methods to enhance the durability of structures;
  • Environmental and operational effects on SHM reliability;
  • Novel multi-functional sensors for structural health monitoring;
  • Structural health monitoring-informed maintenance management.

Dr. Alessandro De Luca
Dr. Donato Perfetto
Dr. Raffaele Ciardiello
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • non-destructive evaluation (NDE)
  • structural health monitoring (SHM)
  • finite element analysis

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

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Research

18 pages, 1727 KiB  
Article
Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
by Deepak Kumar Singh, Dibakar Raj Pant, Ganesh Gautam and Bhanu Shrestha
Appl. Sci. 2025, 15(12), 6687; https://doi.org/10.3390/app15126687 (registering DOI) - 13 Jun 2025
Abstract
Video surveillance is widely used in different areas like roads, malls, education, industries, retail, parks, bus stands, and restaurants, each presenting distinct anomaly patterns that demand specialized detection strategies. Adapting anomaly detection models to new camera viewpoints or environmental variations within the same [...] Read more.
Video surveillance is widely used in different areas like roads, malls, education, industries, retail, parks, bus stands, and restaurants, each presenting distinct anomaly patterns that demand specialized detection strategies. Adapting anomaly detection models to new camera viewpoints or environmental variations within the same scenario remains a significant challenge. Extending these models to entirely different surveillance environments or scenarios often requires extensive retraining, which can be both resource-intensive and time-consuming. To overcome these limitations, model frameworks, i.e., the video anomaly detector model, have been proposed, leveraging the meta-learning framework for faster adaptation using swin transformer for feature extraction to new concepts. In response, the dataset named MSAD (multi-scenario anomaly detection) having 14 different scenarios from multiple camera views, is the high resolution anomaly detection dataset that includes diverse motion patterns and challenging variations such as varying lighting and weather conditions, offering a robust foundation for training advanced anomaly detection models. Experiments validate the effectiveness of the proposed framework, which integrates model-agnostic meta-learning (MAML) with a ten-shot, one-query adaptation strategy. Leveraging the swin transformer as a spatial feature extractor, the model captures rich hierarchical representations from surveillance videos. This combination enables rapid generalization to novel viewpoints within the same scenario and maintains competitive performance when deployed in entirely new environments. These results highlight the strength of MAML in few-shot learning settings and demonstrate its potential for scalable anomaly detection across diverse surveillance scenarios. Full article
(This article belongs to the Special Issue Advances in Performance Monitoring and Anomaly Detection across Structures and Industrial Processes)
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16 pages, 3738 KiB  
Article
Optimization of Adhesive Joint Design in Timber–Glass Systems: Enhancing Structural Performance with Primer Treatment
by Rosa Agliata, Alessandro De Luca, Francesco Caputo, Francesco Marchione, Raffaele Sepe and Placido Munafò
Appl. Sci. 2025, 15(3), 1616; https://doi.org/10.3390/app15031616 - 5 Feb 2025
Viewed by 783
Abstract
The increasing use of large glass surfaces in modern architecture requires robust adhesive solutions that balance aesthetic appeal with structural resilience, particularly in timber–glass applications. This study examines the influence of primer treatments on the shear performance of timber–glass adhesive joints, employing a [...] Read more.
The increasing use of large glass surfaces in modern architecture requires robust adhesive solutions that balance aesthetic appeal with structural resilience, particularly in timber–glass applications. This study examines the influence of primer treatments on the shear performance of timber–glass adhesive joints, employing a combination of experimental testing and simulation techniques. Double-lap shear tests with epoxy adhesives assess the impact of various surface treatments on joint stiffness, shear stress distribution, and deformation. Additionally, a finite element model is developed to simulate joint behavior, evaluate failure modes, and analyze displacement patterns. Results indicate that primer applications notably enhance structural integrity by reducing displacement and increasing joint stability, thereby supporting more durable timber–glass assemblies. These findings offer valuable insights for advancing adhesive technologies in architectural components, enabling a closer alignment between structural performance and design innovation in timber–glass systems. Full article
(This article belongs to the Special Issue Advances in Performance Monitoring and Anomaly Detection across Structures and Industrial Processes)
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17 pages, 5580 KiB  
Article
Revolutionizing Concrete Bridge Assessment: Implementing Nondestructive Scanning for Transformative Evaluation
by Wael Zatar, Felipe Mota Ruiz and Hien Nghiem
Appl. Sci. 2024, 14(24), 11590; https://doi.org/10.3390/app142411590 - 12 Dec 2024
Viewed by 810
Abstract
This study focused on analyzing the impact of ground-penetrating radar (GPR) scan spacing on accurately assessing the reinforcement of concrete bridge girders, providing practical insights. A decommissioned bridge box beam was evaluated to unveil rebars and tendons’ depth and spacing. The box beam [...] Read more.
This study focused on analyzing the impact of ground-penetrating radar (GPR) scan spacing on accurately assessing the reinforcement of concrete bridge girders, providing practical insights. A decommissioned bridge box beam was evaluated to unveil rebars and tendons’ depth and spacing. The box beam was decommissioned from the West Virginia Division of Highways inventory. An innovative algorithm was developed to fully automate the analysis of survey grid data across all sides of the beam. Implementing this algorithm into a computer code has paved the way for comprehensive automation of GPR data analyses. Comparing GPR data analyses from various profile line offsets, this study assists in producing optimal protocols for inspecting box beams. Transverse profile line offsets between 4 in. and 24 in. yielded nearly identical results, setting a new standard for precision. Utilizing more than one longitudinal profile line was highly beneficial in accurately assessing prestressed concrete box beams. This research helps redefine bridge evaluation by precisely finding rebar spacing, concrete cover, and other internal characteristics. This study’s findings offer invaluable advancements and equip state departments of transportation with the knowledge to accurately assess in-service concrete bridge box beams, empowering them to make informed decisions. Full article
(This article belongs to the Special Issue Advances in Performance Monitoring and Anomaly Detection across Structures and Industrial Processes)
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