Advanced Non-Destructive Testing Simulation and Modeling Approaches for Fiber-Reinforced Polymer Pipes: A Review

Современные подходы к моделированию и симуляции неразрушающего контроля труб из армированного волокнами полимера: обзор
Jan Lean Tai, Mohamed Thariq Hameed Sultan, Andrzej Łukaszewicz, Jerzy Jóźwik, Zbigniew Oksiuta, Farah Syazwani Shahar
2025-05-24

Monte Carlo simulationsdigital non-destructive testingfiber-reinforced polymer pipesfinite element methodmachine learning and deep learning
Fiber-reinforced polymer (FRP) pipes have emerged as a preferred alternative to conventional metallic piping systems in various industries, including chemical processing, marine, and oil and gas industries, owing to their superior corrosion resistance, high strength-to-weight ratio, and extended service life. However, ensuring the long-term reliability and structural integrity of FRP pipes presents significant challenges, primarily because of their anisotropic and heterogeneous nature, which complicates defect detection and characterization. Traditional non-destructive testing (NDT) methods, which are widely applied, often fail to address these complexities, necessitating the adoption of advanced digital techniques. This review systematically examines recent advancements in digital NDT approaches with a particular focus on their application to composite materials. Drawing from 140 peer-reviewed articles published between 2016 and 2024, this review highlights the role of numerical modeling, simulation, machine learning (ML), and deep learning (DL) in enhancing defect detection sensitivity, automating data interpretation, and supporting predictive maintenance strategies. Numerical techniques, such as the finite element method (FEM) and Monte Carlo simulations, have been shown to improve inspection reliability through virtual defect modeling and parameter optimization. Meanwhile, ML and DL algorithms demonstrate transformative capabilities in automating defect classification, segmentation, and severity assessment, significantly reducing the inspection time and human dependency. Despite these promising developments, this review identifies a critical gap in the field: the limited translation of advanced digital methods into field-deployable solutions specifically tailored for FRP piping systems. The unique structural complexities and operational demands of FRP pipes require dedicated research for the development of validated digital models, application-specific datasets, and industry-aligned evaluation protocols. This review provides strategic insights and future research directions aimed at bridging the gap and promoting the integration of digital NDT technologies into real-world FRP pipe inspection and lifecycle management frameworks.
1
A major unresolved gap is the limited translation of digital methods into validated, field-deployable solutions tailored to FRP pipes, requiring application-specific datasets and industry-aligned evaluation protocols.
2
Advanced digital NDT methods support enhanced defect detection sensitivity and predictive maintenance for anisotropic and heterogeneous FRP piping systems.
3
Machine learning and deep learning automate defect classification, segmentation, and severity assessment, reducing inspection time and dependence on human interpretation.
4
Numerical methods, including finite element and Monte Carlo simulations, improve inspection reliability through virtual defect modeling and parameter optimization.
5
The review analyzes 140 peer-reviewed studies from 2016–2024 on advanced digital NDT methods for fiber-reinforced polymer pipes.

Fiber-reinforced polymer (FRP) pipes and their inspection and lifecycle management

Application of advanced digital non-destructive testing methods, including numerical modeling, simulation, machine learning, and deep learning, for defect detection, characterization, classification, severity assessment, and predictive maintenance of FRP pipes

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2025-05-24
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Jan Lean Tai
Mohamed Thariq Hameed Sultan
Andrzej Łukaszewicz
Jerzy Jóźwik
Zbigniew Oksiuta
Farah Syazwani Shahar
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