Cross-Bore Risk Mapping Using Machine Learning for Utility Inspection

The patent replaces invasive mechanical detection methods with a non-invasive machine learning-based predictive system. The system uses training data from known cross-bore locations and machine learning algorithms to predict probable cross-bore locations without physically invading or disturbing the underground utility network, thereby eliminating the harmful effects of invasive detection while maintaining detection capability.

The patent introduces an intermediary risk probability map that mediates between the detection goal and the ground truth. Instead of directly detecting cross-bores through invasive means, the system uses machine learning models to create a probabilistic representation of risk areas, which then guides targeted non-invasive inspections only where needed, reducing overall risk while improving detection effectiveness.

The patent applies local quality by transitioning from uniform comprehensive inspection to targeted localized inspection. The machine learning model generates risk probability values for different locations, allowing inspectors to concentrate their time and resources only on high-risk areas where cross-bores are most likely to occur. This maintains high measurement precision for the inspected areas while dramatically reducing overall inspection time.

The patent performs preliminary action by using machine learning models to pre-identify and rank potential cross-bore locations before actual inspection occurs. The system processes training data and generates predictive risk maps in advance, enabling inspectors to prepare and prioritize their inspection routes efficiently, thereby reducing the time required for comprehensive inspection while maintaining accuracy.

The patent implements feedback by using machine learning models that continuously learn from inspection results and cross-bore data. The system processes feedback from actual inspections and cross-bore detections to refine its predictive models, improving reliability over time. This automated feedback loop eliminates subjective human judgment while maintaining ease of operation through automated risk calculation and spatial distribution.

The patent applies self-service by enabling the risk assessment system to autonomously process data, calculate risk probabilities, and generate inspection priorities without human intervention. The machine learning models automatically learn from available data and perform risk assessments independently, providing both ease of operation through automation and reliability through consistent, objective calculations rather than subjective human judgment.