Navigating Uncertainty in Image-Based Structural Health Monitoring: The Role of False Positives, False Negatives, and Base Rate Bias

This study investigates the practical limitations and reliability challenges of image-based structural health monitoring (SHM) systems that utilize machine learning and computer vision to detect damage in infrastructure. While such systems offer scalable and efficient alternatives to manual inspections, their performance is significantly affected by false positives, false negatives, environmental variability, and statistical biases – particularly base rate bias. When actual damage is rare, even highly accurate models may produce an overwhelming number of false alarms, undermining trust in automated detection and leading to inefficient allocation of inspection resources. Using numerical simulations, the paper illustrates how base rate bias reduces precision in low-damage environments and explores the broader implications for system design, operational risk, and decision-making. Several mitigation strategies are presented, including threshold adjustments based on structural criticality, human in-the-loop verification, hybrid monitoring approaches, continuous model updating, and risk based decision frameworks. These findings emphasize the importance of context-aware deployment and uncertainty management in ensuring the effectiveness and real-world applicability of image-based SHM systems.