Infrastructure Degradation Prediction via Segmented ML Models
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Solution Overview
Problem
Current methods for identifying time-specific and location-specific infrastructure degradation in infrastructural systems, such as roadways and rail systems, are inefficient, leading to inadequate resource allocation for maintenance and repair, and lack effective predictive models for infrastructure failure risk.
Innovation Solution
A computer-based method utilizing machine learning techniques, specifically a degradation machine learning model, that processes datasets with time-independent and time-dependent characteristics to predict infrastructure asset conditions and recommend management decisions, incorporating segmentation strategies and feature engineering to enhance prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional inspection and maintenance methods are used, then current infrastructure conditions can be monitored, but time-specific and location-specific degradation prediction capability is insufficient
Solution Approach 1:
The system performs preliminary actions by predicting infrastructure degradation before it occurs using machine learning models trained on historical data. The degradation machine learning model processes time-independent and time-dependent characteristics to forecast future conditions, enabling proactive maintenance scheduling and resource allocation before failures actually happen.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual infrastructure conditions and comparing them against predicted degradation trajectories. This feedback loop allows the machine learning model to be retrained and refined, improving prediction accuracy over time while enabling dynamic adjustment of maintenance strategies based on actual performance data.
2Measurement precision
If comprehensive data collection and machine learning modeling are implemented, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the infrastructure system into discrete asset components and segments, each with its own set of time-independent and time-dependent characteristics. This segmentation allows the machine learning model to process and analyze specific local conditions independently, improving prediction precision for individual locations while managing overall system complexity through modular analysis.
Solution Approach 2:
The system implements local quality by tailoring the analysis and prediction parameters to specific locations and asset types. Different infrastructure segments receive customized feature engineering and model parameters based on their unique characteristics, such as traffic patterns, environmental conditions, and historical failure data, thereby improving local prediction accuracy without requiring the entire system to handle all possible scenarios.
3Productivity
If time-specific and location-specific analysis is performed, then maintenance optimization is achieved, but data processing requirements increase
Solution Approach 1:
The system extracts and isolates specific time-independent and time-dependent characteristics from the broader infrastructure data. By identifying and focusing on the most predictive features for each asset type and location, the system reduces the overall data processing volume required while maintaining high prediction accuracy. This extraction process filters out redundant information and concentrates computational resources on critical degradation indicators.
Data Source
AI summary
Systems and methods of present disclosure provide a processor to receive a first dataset with time-independent characteristics of infrastructure assets of an infrastructural system, and a second dataset with time-dependent characteristics of the infrastructure assets. The processor segments the infrastructural system into the infrastructure assets having a variety of asset components. The processor generates data records for each infrastructure asset where each data record includes a subset of the first dataset and a subset of the second dataset. Using the data records, the processor generates a set of features which are input into a degradation machine learning model. The processor receives an output from the degradation machine learning model indicative of a prediction of a condition of a portion of the infrastructural system at a predetermined time and renders on a graphical user interface a representation of a location, the condition and a recommended asset management decision.