Advances in Artificial Intelligence and Computational Methods for Prognostics and Health Management of Civil and Mechanical Systems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 706
Special Issue Editors
Interests: prognostics and health management (PHM); artificial intelligence; biomimetic actuator; adaptive structures; structural analysis; structural optimization; numerical analysis; composite structures
Special Issues, Collections and Topics in MDPI journals
Interests: prognostics and health management; artificial intelligence; composite structures; intelligent machines
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to highlight recent developments in mathematical modeling, artificial intelligence (AI), and physics-informed machine learning for prognostics and health management (PHM). It focuses on applications to mechanical system and civil infrastructure, which are critical to sectors such as transportation, energy, and water. As these systems age, there is a growing need for accurate diagnostics, degradation modeling, and failure prediction. The integration of AI with physics-based approaches enables more reliable health monitoring, remaining useful life estimation, and decision-making under uncertainty. This Issue seeks contributions that advance computational methods for PHM and support sustainable and resilient infrastructure management.
We invite original research and comprehensive review articles that highlight the use of computational intelligence, data-driven and hybrid physics-informed models, and probabilistic frameworks for damage detection, localization, and remaining useful life (RUL) estimation.
Topics of interest include, but are not limited to, the following:
- Mathematical foundations for PHM and failure modeling.
- Artificial intelligence and machine learning for structural and mechanical health monitoring.
- Physics-informed neural networks and hybrid modeling techniques.
- Prognostics of rotating machines (e.g., turbines, pumps, motors).
- Damage detection and localization in civil infrastructure (e.g., bridges, pipelines, pavements).
- Uncertainty quantification and probabilistic forecasting in PHM.
- Computational models for sensor fusion and anomaly detection.
- Real-time condition monitoring systems and digital twin frameworks.
Prof. Dr. Heung Soo Kim
Dr. Salman Khalid
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. Mathematics 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 2600 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
- artificial intelligence
- mechanical system
- civil infrastructure
- computational modeling
- physics-informed machine learning
- structural health monitoring
- failure prediction
- damage detection
- prognostics and health management
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