Advancements in Modern Smart Techniques for Structural Health Monitoring (SHM) of FRP Pipelines

Dear Colleagues,

Fiber reinforced polymer (FRP) Pipelines play an important role in gas and liquids transportation over long distances. However, due to the harsh environment of the construction locations, FRP pipelines always suffer from structural damage problems. In recent years, nondestructive evaluation (NDE) approaches have been proposed for damages detection in FRP pipelines. However, due to the different needs for heat or wave sources and complicated data analyzers of different NDE methods, such applications can encounter various difficulties in terms of operational and monitoring requirements. Moreover, it may not be possible to achieve thorough monitoring of the wide fields. Smart monitoring techniques of structures damages has attracted increasing applications in the last few years. An artificial intelligence (AI) based methodologies, including machine learning, Deep learning methods, have been proposed for model updating, diagnostics, data interpretation and feature extraction for the heath monitoring of mechanical systems. This rapidly emerging field of research has demonstrated superiority for system identification, feature extraction, damage identification and even direct response prediction of dynamical systems and has shown promises for a wide range of practical applications.

This Special Issue focuses on the advances in smart techniques for structural health monitoring (SHM) and damages identification of FRP pipelines. In addition, it introduce of AI based methodologies for structural health monitoring of FRP pipeline systems and the analysis and feature extraction from FRP pipelines response data. Studies concerning NDE techniques, artificial intelligence for SHM of FRP pipelines. Based Methods and related fields are all welcome, both numerical and experimental. Potential topics include, but are not limited to, the following areas:

• NDE techniques for FRP Pipelines diagnosis;
• FRP Pipelines Damages modeling and simulation;
• FRP Pipelines Damages Identification;
• Smart techniques for SHM of FRP Pipelines;
• Surrogate Models for FRP Pipelines diagnosis;
• Optimization techniques for FRP Pipelines;
• Artificial Intelligence (AI) based schemes for SHM of FRP Pipelines,
• Deep Neural Networks;
• Various Machine Learning Tools;
• Probabilistic Methods for SHM combined with AI Methods;
• Feature Extraction Schemes of FRP pipelines response data.

Dr. Wael A. Altabey
Prof. Dr. Mohammad Noori
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. Journal of Composites Science 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.

• FRP pipelines
• structural health monitoring
• artificial intelligence
• machine learning
• deep learning
• damage identification
• feature extraction