A warping function-based control chart for detecting distributional changes in damage-sensitive features for structural condition assessment

2026-01-18

AI Summary

This study addresses the limitations of traditional control charts in effectively detecting complex deformations in the distribution of structural damage–sensitive features and their insufficient robustness to data contamination. To overcome these challenges, a novel nonparametric control chart is proposed that models shape variations of probability density functions as warping functions, enabling unified online monitoring of both location shifts and higher-order distributional deformations within a functional data analysis framework. The method uniquely leverages warping functions to characterize the evolution of distributional morphology, offering simultaneous sensitivity to mean/variance shifts and intricate shape changes while maintaining robustness against outliers. Comprehensive validation through numerical simulations and field measurements from stay cables of a long-span cable-stayed bridge demonstrates significantly superior damage detection performance compared to existing approaches.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Data-driven damage detection methods achieve damage identification by analyzing changes in damage-sensitive features (DSFs) derived from structural health monitoring (SHM) data. The core reason for their effectiveness lies in the fact that damage or structural state transition can be manifested as changes in the distribution of DSF data. This enables us to reframe the problem of damage detection as one of identifying these distributional changes. Hence, developing automated tools for detecting such changes is pivotal for automated structural health diagnosis. Control charts are extensively utilized in SHM for DSF change detection, owing to their excellent online detection and early warning capabilities. However, conventional methods are primarily designed to detect mean or variance shifts, making it challenging to identify complex shape changes in distributions. This limitation results in insufficient damage detection sensitivity. Moreover, they typically exhibit poor robustness against data contamination. This paper proposes a novel control chart to address these limitations. It employs the probability density functions (PDFs) of subgrouped DSF data as monitoring objects, with shape deformations characterized by warping functions. Furthermore, a nonparametric control chart is specifically constructed for warping function monitoring in the functional data analysis framework. Key advantages of the new method include the ability to detect both shifts and complex shape deformations in distributions, excellent online detection performance, and robustness against data contamination. Extensive simulation studies demonstrate its superiority over competing approaches. Finally, the method is applied to detecting distributional changes in DSF data for cable condition assessment in a long-span cable-stayed bridge, demonstrating its practical utility in engineering.

Problem

• distributional changes
• damage-sensitive features
• control chart
• structural health monitoring
• data contamination

Innovation

• warping function
• control chart
• distributional change detection
• structural health monitoring
• nonparametric method