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 R²>0.95), progress is constrained by ten persistent gaps:
- Scarcity and low quality of field benchmark datasets;
- Overreliance on lab/simulation with limited real-world validation;
- Lack of standardized evaluation protocols for fair comparison;
- Opaque "black-box" models hindering trust and certification;
- Underutilized multi-sensor integration;
- Computational scalability limits for network-scale use;
- Narrow subsystem focus without full-lifecycle coverage;
- Weak cross-domain generalization across regions/materials;
- Insufficient uncertainty quantification for risk-aware decisions;
- Neglect of regulatory, ethical, and operational adoption paths.
Three research frontiers emerge to drive industrial deployment:
- Large-scale multi-source benchmark datasets with field failure labels;
- Physics-informed and interpretable ML frameworks bridging mechanics and algorithms;
- Standardized evaluation protocols and field-level validation schemes aligned with codes (API, ASME, DNV).
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.
