Bivariate Regressive Adaptive INdex for Structural Health Monitoring: Performance Assessment and Experimental Verification

摘要

This study focuses on embeddable algorithms that operate within multi-scale wireless sensor networks for damage detection in civil infrastructure systems, and in specific, the Bivariate Regressive Adaptive INdex (BRAIN) to detect damage in structures by examining the changes in regressive coefficients of time series models. As its name suggests, BRAIN exploits heterogeneous sensor arrays by a data-driven damage feature (DSF) to enhance detection capability through the use of two types of response data, each with its own unique sensitivities to damage. While previous studies have shown that BRAIN offers more reliable damage detection, a number of factors contributing to its performance are explored herein, including observability, damage proximity/severity, and relative signal strength. These investigations also include an experimental program to determine if performance is maintained when implementing the approaches in physical systems. The results of these investigations will be used to further verify that the use of heterogeneous sensing enhances overall detection capability of such data-driven damage metrics.