Graph-Based Risk Assessment for Infrastructure Interdependencies
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Solution Overview
Problem
Current risk management systems for infrastructure fail to account for non-normal operations, interdependencies between infrastructure systems, and the impact of unpredictable factors like human errors, leading to inadequate risk assessments that neglect potential cascading failures and rare but catastrophic events.
Innovation Solution
An automated system that aggregates data from various sources using a graph-based database structure, incorporating spatio-temporal and ontological reasoning to continuously monitor and assess risks in real-time, accounting for non-normal operations and complex interactions between infrastructure systems, and providing probabilistic risk analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional quantitative risk assessment systems operate as human-driven passive systems with normal operation assumptions, then the systems are simpler to operate, but they fail to account for non-normal operations, interdependencies between infrastructure systems, and unpredictable factors leading to inadequate risk assessments
Solution Approach 1:
The system enables automated risk assessment that operates independently without continuous human intervention. The automated system continuously monitors infrastructure data, performs risk calculations, and generates assessments autonomously, eliminating the need for manual operation while improving reliability through consistent automated analysis of complex interdependencies
Solution Approach 2:
The system integrates multiple data sources and analysis methods into a composite risk assessment framework. It combines data from GIS systems, database systems, sensor networks, and multiple risk models to create a comprehensive assessment that accounts for non-normal operations, interdependencies, and unpredictable factors, thereby improving reliability through diversified information integration
2Reliability
If conventional systems provide a single point estimate of risk, then the output is simpler to interpret, but they fail to provide the range and likelihood of risk or hazard
Solution Approach 1:
The system segments risk assessment into multiple probability levels and risk categories instead of providing a single point estimate. It divides the risk spectrum into ranges with associated likelihoods, allowing stakeholders to understand both the magnitude and probability of potential risks, thereby preserving complete risk information while making it systematically organized and interpretable
3Reliability
If traditional risk models assume normal operations in infrastructure, then the models are easier to implement, but they neglect weak events that may ultimately be precursor to future catastrophic events
Solution Approach 1:
The system performs preliminary identification and monitoring of weak events and precursors before they develop into catastrophic failures. By continuously analyzing infrastructure data and recognizing early warning signs, the system enables preventive action to be taken before critical failures occur, improving risk detection capability while managing model complexity through automated pattern recognition
Solution Approach 2:
The system changes the operational parameters assumed in risk models from normal operation conditions to include non-normal operations, extreme events, and boundary conditions. This allows the models to account for a broader range of scenarios including weak events and precursors, improving reliability by capturing rare but critical risk scenarios
4Reliability
If risk assessment systems lack tight integration with GIS and database systems, then the systems are simpler to maintain, but they cannot access the latest data on infrastructures
Solution Approach 1:
The system creates a universal integrated platform that simultaneously accesses and processes data from multiple sources including GIS systems, database systems, sensor networks, and external data feeds. This multi-functional integration enables the system to retrieve and analyze the latest infrastructure data across diverse formats and sources, ensuring data currency while managing integration complexity through standardized data access protocols
Data Source
AI summary
An automated and intelligent quantitative system and method for assessing risk in infrastructure systems including, but not limited to, gas, electric, water, sewer, transportation, and/or telecommunication systems. The invention incorporates a graph-based data structure of multiple infrastructure systems. The graph-based data structure includes a multi-layered structure with each layer having nodes for components of one of the multiple infrastructure systems, and edge links between related pairs of the nodes. A spatio-temporal and ontological reasoner performs spatial and temporal reasoning on the graph-based data structure to identify nodes of the graph-based data structure likely affected by the new activity event.