A review of machine learning advances in reliability-based design, integrity assessment, and pipeline inspection and maintenance

Newly published systematic review Pipeline Science and Engineering Journal Map advances in machine learning (ML) across the pipeline lifecycle. These include reliability-based design, structural integrity assessment, condition monitoring, inspection planning, and maintenance decision support. This is the first review to use a lifecycle framework to synthesize 95 core studies and quantify consensus gaps across 24 previous reviews.

This review reveals a methodological shift from traditional case-specific supervised learning to transferable, hybrid, metaheuristic, and physically informed ML techniques. These frameworks decompose signals, quantify uncertainties, use graph-based knowledge representations, and embed physical laws that increase generalizability, from theory-based features and architectures to soft constraint enforcement.

During the reliability design and safety evaluation stages, ML-enhanced probabilistic frameworks (e.g., LFS-SSA-BPNN, LSBES-ELM, GC-GAN+RF) significantly reduce computational costs while maintaining Monte Carlo-level accuracy. Generative models and heuristic optimizers reduce data scarcity and noise, and SHAP/LIME tools make black-box risk models acceptable to regulators.

For structural integrity and deterioration modeling, ML surrogates (GBRT, RF, TGNN, PINN, etc.) replace expensive FEA/SPH simulations with hundreds to 10,000 times faster and near-physical fidelity for burst/collapse pressures, corrosion growth, crack propagation, and geohazard strains. Hybrid physics and ML and residual learning outperform traditional codes such as DNV and API by correcting biases in the model format.

For inspection and maintenance planning, LiDAR, CCTV, AE, MFL, and multi-sensor fusion combined with CNN, Transformer, GNN, and isolation forests enable high-precision defect detection, localization, and classification in noise. Spatial ML+GIS supports hotspot mapping and inspection prioritization, while DRL and Bayesian networks dynamically optimize maintenance intervals and network reliability.

Nevertheless, despite the high accuracy (many models R²>0.95), progress is limited by 10 permanent gaps.

• Scarcity and low quality of field benchmark datasets.
• Over-reliance on lab/simulation with limited real-world validation.
• Lack of standardized evaluation protocols for fair comparisons.
• Opaque “black box” models hinder trust and authentication.
• Underutilized multi-sensor integration.
• Limits on scalability of computations when used at network scale.
• Focus on subsystems rather than covering the entire lifecycle.
• Weak cross-domain generalization across regions/materials.
• Insufficient quantification of uncertainty to make risk-aware decisions.
• Neglect of regulatory, ethical, and operational implementation paths.

Three research frontiers emerge that will drive industrial development.

• A large multi-source benchmark dataset with field fault labels.
• A physics-based, interpretable ML framework that bridges mechanisms and algorithms.
• Field-level validation schemes aligned to standardized assessment protocols and codes (API, ASME, DNV).

The review concludes with a decision matrix roadmap to align researchers, operators, and regulators. Prioritize ML that has physical constraints, recognizes uncertainty, and is integrated into the lifecycle. Position ML as a coordinated surrogate layer for updating code input, rather than replacing standards. It also combines predictive accuracy and reliability metrics, cost-benefit analysis, and auditability for regulatory compliance.

The authors note that future ML-PIM systems will evolve into physically consistent, self-adaptive digital twins that enable online monitoring, predictive maintenance, and continuous reliability assessment to support safe, resilient, and sustainable energy transportation pipelines around the world.

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Contact the author: Ardeshir Savari, Department of Mechanical Engineering, Petroleum University of Technology, Ahvaz, Iran, savari.ardeshir@gmail.com

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