Oversight of U.S. airports under FAA Part 139 is primarily checklist-driven, whereas the ICAO Safety Management System (SMS) emphasizes proactive, performance-based safety practices. Despite widespread adoption of both approaches, quantitative comparisons remain limited. This study develops a structured mapping framework aligning FAA Part 139 requirements with the four ICAO SMS pillars: safety policy, risk management, safety assurance, and safety promotion. Publicly available FAA inspection findings were normalized using OPSNET operational exposure data and compared with ICAO’s Universal Safety Oversight Audit Programme (USOAP) indicators to identify areas of overlap and divergence and to assess whether checklist compliance and SMS routines produce consistent outcomes. The study presents a detailed crosswalk between the two frameworks, descriptive statistics on inspection deficiencies, and comparative assessments of regulatory coverage. Regression modeling evaluates the relationship between SMS implementation levels and FAA inspection outcomes while controlling for airport size and operational context. Findings indicate that Part 139 oversight performs effectively in high-consequence areas, including Aircraft Rescue and Firefighting (ARFF), while SMS contributes to early hazard detection and strengthens safety culture. Based on these results, a hybrid oversight model integrating mandatory inspections with predictive SMS practices is proposed to enhance U.S. aviation safety and support alignment with international standards.

Reliable prediction of tunneling-induced ground settlement in coastal environments is challenged by coupled hydro-mechanical processes, site heterogeneity, and limited labeled data in new projects. This study presents a physics-informed and transferable deep learning framework that embeds dual physical constraints (Peck-type settlement envelope and Darcy-based pore-pressure consistency) within a dynamic transfer learning architecture and an actionable explainable-AI (XAI) layer. The model fuses heterogeneous, time-synchronized streams from tunnel boring machines, geotechnical sensors, and groundwater monitoring systems, adapting across segments and sites through similarity-aware transfer. Applied to a coastal shield-tunneling project (~ 80 000 samples), the framework achieved \({R}^{2}\)= 0.95, RMSE = 1.5 cm, MAE = 1.2 cm, outperforming empirical regressions and unconstrained deep baselines. Ablation studies confirm that physics regularization enhances calibration and stability, while transfer learning improves cross-site generalization. For field deployment, the end-to-end pipeline operates in a 30 s cycle with median latency ≈ 1.6 s and peak inference time ≈ 0.05 s/sample (≈ 20 predictions/s). The XAI layer translates feature attributions into actionable engineering guidance—such as adjusting shield pressure, advance rate, or dewatering—to enable interpretable, auditable decision-making. These findings highlight physics-regularized, explainable transfer learning as a practical route toward trustworthy AI for geotechnical risk prediction in coastal tunneling.