Dynamic Condition Index Model for Building Component Lifecycle Prediction
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Solution Overview
Problem
Current infrastructure asset management systems, such as BUILDER®, face challenges in accurately predicting the condition and lifecycle of building components, leading to inefficient maintenance scheduling and increased costs due to the variability of environmental and usage factors affecting component performance.
Innovation Solution
A dynamic mathematical model that adjusts the condition index (CI) of building components over time using empirical data and Weibull distribution, allowing for self-correction based on inspection data and localized conditions, enabling proactive maintenance planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed service life is assumed for building components, then the asset management process is simplified, but the accuracy of condition prediction and maintenance scheduling deteriorates due to environmental and usage variability
Solution Approach 1:
The patent applies dynamics by transitioning from fixed service life assumptions to dynamic service life predictions. The system continuously updates condition predictions based on actual inspection data, environmental factors, and usage conditions, allowing the service life model to adapt and evolve over time rather than remaining static
Solution Approach 2:
The patent implements feedback mechanisms where actual inspection results are fed back into the condition prediction model. This feedback loop allows the system to learn from real-world performance data, adjust predictions accordingly, and improve accuracy over time by comparing predicted versus actual component conditions
2Loss of energy
If maintenance is delayed to reduce costs, then short-term expenses are reduced, but long-term costs increase exponentially due to accelerated condition deterioration
Solution Approach 1:
The patent applies preliminary action by predicting future component conditions and scheduling maintenance before actual deterioration occurs. The condition prediction model identifies trends and forecasts when components will reach critical thresholds, enabling proactive maintenance planning that prevents expensive emergency repairs and extends component life
Solution Approach 2:
The patent enables skipping unnecessary maintenance activities by accurately predicting which components will actually deteriorate and when. The system avoids premature maintenance on components that are not yet approaching failure thresholds, optimizing resource allocation and reducing unnecessary maintenance costs
3Measurement precision
If detailed condition assessment is performed at component-section level, then maintenance precision is improved, but the time and resources required for inspection increase
Solution Approach 1:
The patent applies segmentation by dividing the building into hierarchical levels (building → system → component → component-section). This segmentation allows the condition prediction model to focus computational resources on specific components of interest rather than assessing every component uniformly, reducing overall inspection time while maintaining precision for critical elements
Solution Approach 2:
The patent implements partial action by performing detailed condition assessments only on components that are predicted to approach critical thresholds within a specified time horizon. The model identifies and prioritizes components requiring detailed inspection, avoiding unnecessary detailed assessments of components that are not yet approaching failure conditions
Data Source
AI summary
Initial assumptions related to the service life of a particular item, such as a component section of a building, are mathematically modeled to construct an initial lifecycle condition relationship as condition index (CI) v. time. To update the model, empirical data may be input at any time. As modeled in an engineering management system, for example, inspections are performed on the item to verify actual condition with that predicted. Quantitative inspection data are then used to update the initial curve. As inspections are performed and data recorded, the curve is updated to accurately capture observed condition and provide realistic estimates of predicted condition, and expected service life. In select embodiments of the present invention, empirical data, such as that from inspections, are weighted, e.g., inspection data may be weighted based on type, level of detail, time in service, time since last inspection and the like.