Toward a Digital Twin: A Spatiotemporal Data Integration Framework for Pipeline Risk Assessment

Abstract Buried pipelines face threats from electrochemical corrosion, dynamic geological, and geo-hydrological hazards. Current fragmented datasets and associated standards hinder streamlined and comprehensive one-stop risk assessment. This paper proposes a dynamic database framework that integrates pipeline physical attributes with electrochemical metrics (e.g., close-interval potential surveys, DC voltage gradients, in-line inspection data), geohazard indicators (earthquake events, landslide susceptibility indices, shear-wave velocity profiles), and hydrological factors (river proximity, channel-scour metrics). Automated data harvesters leverage FDSN web services, United States Geological Survey (USGS) APIs, and Google Earth Engine to continuously ingest and normalize new datasets when available. This framework eliminates fragmentation across traditionally isolated data sources by providing a unified, spatiotemporally indexed platform that supports complex queries for corrosion, geological, and hydrological related pipeline risk modeling and visualization. The primary outputs is a list of geo-referenced objects, where each pipeline segment is represented as a spatial feature enriched with multi-factor risk attributes. This enables rapid visualization in QGIS, ArcGIS, or web libraries like Leaflet, and supports advanced spatial operations such as hotspot detection and proximity-based alerts. The standardized format allows integration with rule-based systems and machine learning processing pipelines. Ultimately, this extensible, scalable framework lays the foundation for pipeline digital twin applications and data-driven pipeline integrity management.