A systematic review newly published in the Journal of Pipeline Science and Engineering maps machine learning (ML) advances for pipelines across the full lifecycle: reliability-based design, structural integrity evaluation, condition monitoring, inspection planning, and maintenance decision support. It is the first review to synthesize 95 core studies using a lifecycle framework and quantify consensus gaps across 24 prior reviews.
The review reveals the methodological shift from conventional case-specific supervised learning toward transferable, hybrid, metaheuristic, and physics-informed ML techniques. These frameworks decompose signals, quantify uncertainty, use graph-based knowledge representation, and embed physical laws to boost generalizability—ranging from theory-guided features and architectures to soft constraint enforcement.
At the reliability design and safety assessment stage, ML-enhanced probabilistic frameworks (e.g., LFS-SSA-BPNN, LSBES-ELM, GC-GAN+RF) maintain Monte Carlo-level accuracy while drastically cutting computation cost; generative models and heuristic optimizers mitigate data scarcity and noise, while SHAP/LIME tools open black-box risk models for regulatory acceptance.
For structural integrity and degradation modeling, ML surrogates (e.g., GBRT, RF, TGNN, PINNs) replace costly FEA/SPH simulations, delivering near-physics fidelity with hundreds-to-ten-thousand-fold speedups for burst/collapse pressure, corrosion growth, crack propagation, and geohazard-induced strain. Physics-ML hybrids and residual learning outperform traditional codes like DNV and API by correcting model-form biases.
In inspection and maintenance planning, LiDAR, CCTV, AE, MFL, and multi-sensor fusion paired with CNN, Transformer, GNN, and isolation forest enable high-precision defect detection, localization, and classification under noise. Spatial ML+GIS supports hotspot mapping and inspection prioritization, while DRL and Bayesian networks optimize maintenance intervals and network reliability dynamically.
Nonetheless, despite high accuracy (many models achieve R ²>0.95), progress is constrained by ten persistent gaps:
Three research frontiers emerge to drive industrial deployment:
The review concludes with a decision-matrix roadmap aligning researchers, operators, and regulators: prioritize physics-constrained, uncertainty-aware, and lifecycle-integrated ML; position ML as a calibrated surrogate layer to update code inputs rather than replace standards; and couple predictive accuracy with reliability metrics, cost-benefit analysis, and auditability for regulatory compliance.
The authors note that future ML-PIM systems will evolve into physics-consistent, self-adaptive digital twins enabling online monitoring, predictive maintenance, and continuous reliability assessment—supporting safe, resilient, and sustainable energy transport pipelines worldwide.
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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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Journal of Pipeline Science and Engineering
Literature review
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State-of-the-art Machine Learning Advances in Reliability-based Design, Integrity Assessment, Inspection and Maintenance of Pipelines: A Systematic Review
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.