Infrastructure Resilience Estimation via Machine Learning
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Solution Overview
Problem
Predicting and mitigating the risk of failure in complex infrastructural systems is challenging due to their spatial distribution, diverse components, and varying environmental exposures, making maintenance management difficult and costly.
Innovation Solution
A computer-based system that uses machine-learning models trained on historical data to assess the resilience of infrastructural components by modeling their exposure to hazardous conditions, providing vulnerability metrics and recommending maintenance tasks to reduce the risk of failure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional maintenance management methods are used for infrastructural systems, then operational simplicity is maintained, but reliability deteriorates due to inability to predict failures in complex multi-component systems with varying environmental exposures
Solution Approach 1:
The patent segments the complex infrastructural system into individual components, each assessed independently for failure risk. The system evaluates each component's exposure to hazardous conditions, component condition, and vulnerability separately, then aggregates these assessments to determine overall system reliability. This segmentation allows manageable analysis of complex systems by breaking them into assessable units.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary that processes multiple input factors (hazardous condition exposure, component condition, vulnerability metrics) and transforms them into predictive failure risk assessments. This intermediary model synthesizes complex multi-factor data into actionable reliability predictions, resolving the contradiction between system complexity and reliable assessment.
2Measurement precision
If comprehensive environmental and component data are collected to improve failure prediction accuracy, then measurement precision improves, but device complexity increases due to multiple data collection and processing requirements
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: it processes hazardous condition exposure data, component condition data, and vulnerability metrics; performs feature selection and weighting; generates failure risk predictions; and identifies key risk factors. This multi-functionality consolidates complex data processing tasks into a single unified system, improving measurement precision without proportionally increasing device complexity.
Solution Approach 2:
The system transforms raw environmental and component data into standardized vulnerability metrics and risk scores through parameter transformation. The machine learning model adjusts parameter weights and relationships based on historical data, converting diverse input parameters into a unified failure risk assessment framework that improves precision while managing complexity through parameter standardization.
3Reliability
If proactive maintenance based on failure risk prediction is implemented, then reliability improves, but loss of time increases due to detailed assessment and analysis requirements
Solution Approach 1:
The system performs preliminary assessment of infrastructure components by evaluating hazardous condition exposure, component condition, and vulnerability metrics before failures occur. The machine learning model pre-processes and stores vulnerability data and historical performance information, enabling rapid failure risk prediction when needed. This preliminary action reduces assessment time during actual maintenance decision-making while maintaining high reliability predictions.
Solution Approach 2:
The system creates simplified copies or representations of complex infrastructure components through vulnerability metrics and risk scores. Instead of performing detailed physical inspections for every assessment, the machine learning model uses replicated data structures and historical patterns to quickly estimate failure risks. This copying approach maintains prediction accuracy while significantly reducing assessment time.
Data Source
AI summary
Systems and methods for assessing the resilience of an infrastructural system. The system includes a computer program product stored on non-transitory machine-readable media. The media includes machine executable instructions for implementing a method for assessing the resilience of an infrastructural system. The method includes receiving, from an interface, scenario specific data regarding conditions for the infrastructural system; processing the scenario specific data to determine a vulnerability metric for a probability of failure for a component of the infrastructural system; determining, based on the vulnerability metric, an in-situ failure risk of the infrastructural system incorporating a complexity of environmental conditions; determining, based on the vulnerability metric, a recommended maintenance task for the component of the infrastructural system; and providing the in-situ failure risk of the infrastructural system and the recommended maintenance task for the component to the interface.