Tai Jan Lean, Sultan Mohamed Thariq Hameed, Łukaszewicz Andrzej, Józwik Jerzy, Oksiuta Zbigniew, Shahar Farah Syazwani
Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.
Laboratory of Biocomposite Technology, Institute of Tropical Forest and Forest Product (INTROP), Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.
Materials (Basel). 2025 May 24;18(11):2466. doi: 10.3390/ma18112466.
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.
纤维增强聚合物（FRP）管道已成为包括化学加工、海洋和石油天然气行业在内的各行业中传统金属管道系统的首选替代品，这得益于其卓越的耐腐蚀性、高强度重量比和延长的使用寿命。然而，确保FRP管道的长期可靠性和结构完整性面临重大挑战，主要是因为其各向异性和非均匀性，这使得缺陷检测和表征变得复杂。广泛应用的传统无损检测（NDT）方法往往无法应对这些复杂性，因此需要采用先进的数字技术。本综述系统地研究了数字无损检测方法的最新进展，特别关注其在复合材料中的应用。本综述参考了2016年至2024年间发表的140篇同行评议文章，强调了数值建模、模拟、机器学习（ML）和深度学习（DL）在提高缺陷检测灵敏度、自动化数据解释以及支持预测性维护策略方面的作用。数值技术，如有限元法（FEM）和蒙特卡罗模拟，已被证明可通过虚拟缺陷建模和参数优化来提高检测可靠性。同时，ML和DL算法在自动化缺陷分类、分割和严重程度评估方面展现出变革性能力，显著减少了检测时间和对人工的依赖。尽管有这些令人鼓舞的进展，但本综述指出了该领域的一个关键差距：先进数字方法在专门针对FRP管道系统的现场可部署解决方案中的转化有限。FRP管道独特的结构复杂性和运行要求需要进行专门研究，以开发经过验证的数字模型、特定应用的数据集和符合行业标准的评估协议。本综述提供了战略见解和未来研究方向，旨在弥合这一差距，并促进数字无损检测技术融入实际的FRP管道检测和生命周期管理框架。
