Toward a Digital Twin: A Spatiotemporal Data Integration Framework for Pipeline Risk Assessment (C2026-00216)

Sreelakshmi Sreeharan, Hui Wang, Kiranmayee Madhusudhan, Sasha George, Homero Castaneda, Yating Yang, Lei Wang, Jay Shah, Hao Wang,

Buried pipelines face threats from electrochemical corrosion and dynamic geo-hydrological hazards, current fragmented datasets hinder comprehensive risk assessment. We present 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, USGS APIs, and Google Earth Engine to continuously ingest and normalize new datasets. This framework eliminates fragmentation across traditionally isolated data sources by providing a unified, spatiotemporally indexed platform that supports complex queries for real-time risk modeling and visualization. It includes a schema capable of accommodating heterogeneous data types and enforcing consistent metadata conventions. The primary output is a GeoPandas “geo object,” 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 pipelines. Ultimately, this extensible, scalable framework lays the foundation for digital twin applications and data-driven pipeline integrity management.