AI Agentic Workflow for Landslide and Subsidence Risk to Pipelines

This workflow automates the detection of millimeter-scale ground displacement using InSAR satellite data, predicting landslide or subsidence events weeks or months before they threaten pipe integrity. By scheduling targeted digs at high-priority sites, you prevent catastrophic ruptures that incur multi-million dollar costs in environmental remediation, regulatory fines, service interruption, and emergency response.

Replaces blanket, schedule-based dig programs with a condition-based, risk-prioritized inspection model. AI agents correlate displacement trends with rainfall, soil data, and historical failures to score and rank thousands of pipeline segments. This directs capital and crew time to the ~5-10% of sites representing 80% of the rupture risk, dramatically improving capital efficiency.

Automates the entire data-to-decision loop: from satellite data ingestion and trend analysis to automated work package generation in systems like SAP Plant Maintenance or Oracle Primavera. This eliminates weeks of manual data correlation and report writing by geotechnical engineers, allowing them to focus on validating high-confidence alerts and designing mitigations.

Creates a continuous, auditable monitoring system that satisfies PHMSA and other regulator requirements for pipeline integrity management (IMP). Every risk prediction, data point, and mitigation action is logged with rationale, providing a defensible record that demonstrates proactive duty-of-care and can reduce liability in incident investigations.

By identifying and mitigating ground movement stress early, the workflow prevents the accelerated fatigue and coating damage that leads to premature pipe replacement. This preserves asset integrity, defers major capital expenditure in pipeline replacement projects, and improves the long-term return on existing infrastructure assets.

The agentic architecture (built on frameworks like LangGraph) allows the workflow to scale across thousands of pipeline miles and multiple hazard types. It creates a unified geohazard command center, integrating InSAR with other data like LiDAR and IoT sensor feeds, turning fragmented monitoring into a centralized, operational intelligence layer for the entire pipeline network.

This agent automates the secure ingestion and normalization of millimeter-scale ground displacement data from satellite-based Interferometric Synthetic Aperture Radar (InSAR) providers. It handles geospatial alignment, filters atmospheric noise, and creates a clean, time-series dataset of pipeline corridor movement, transforming raw satellite feeds into an analysis-ready asset health signal.

A core analytical agent that fuses InSAR displacement trends with external data layers: historical rainfall, soil type maps, slope inclination, and seismic activity. It applies physics-informed ML models to correlate movement triggers, predict acceleration, and assign a quantitative integrity risk score to each pipeline segment. This automates the manual geotechnical analysis that typically delays risk assessment.

This orchestrator agent translates high-risk scores into operational actions. It queries the pipeline GIS for asset details, checks crew availability in the field force management system, and automatically generates prioritized dig site inspection tickets in the CMMS (e.g., SAP, IBM Maximo). It includes recommended actions, required equipment, and integrates approval gates for high-cost interventions.

A critical control agent that routes extreme-risk predictions or complex scenarios requiring engineering judgment to a human review dashboard. It presents all correlated evidence (displacement charts, rainfall history, asset specs) and captures approval or override rationale, ensuring auditability. This gate prevents fully autonomous dig decisions, maintaining required engineering oversight.

This integration agent ensures the workflow's outputs enrich the system of record. It automatically updates risk scores in the PIMS (e.g., DNV Synergi, ESRI), logs all generated inspections and their findings, and closes the loop by feeding field validation results (e.g., 'no issue found', 'repair completed') back into the risk model for continuous learning.

An observability agent that tracks workflow efficacy by comparing predictions to actual field findings and incident reports. It monitors for model drift in the risk-scoring engine and automatically triggers retraining pipelines when prediction accuracy degrades, using new InSAR data and ground truth. This ensures the system adapts to changing geological and climatic conditions.