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.
This study focuses on data-driven methods for structural health monitoring and introduces a Bivariate Regressive
Adaptive INdex (BRAIN) for damage detection in a decentralized, wireless sensor network. BRAIN utilizes a dynamic
damage sensitive feature (DSF) that automatically adapts to the data set, extracting the most damage sensitive model
features, which vary with location, damage severity, loading condition and model type. This data-driven feature is key to
providing the most flexible damage sensitive feature incorporating all available data for a given application to enhance
reliability by including heterogeneous sensor arrays. This study will first evaluate several regressive-type models used
for time-series damage detection, including common homogeneous formats and newly proposed heterogeneous
descriptors and then demonstrate the performance of the newly proposed dynamic DSF against a comparable static DSF.
Performance will be validated by documenting their damage success rates on repeated simulations of randomly-excited
thin beams with minor levels of damage. It will be shown that BRAIN dramatically increases the detection capabilities
over static, homogeneous damage detection frameworks.
The aging Civil Infrastructure System (CIS) in the United States has prompted the need for more effective structural health monitoring (SHM) techniques. Global Positioning Systems (GPS) have shown great promise for SHM, as they allow the total displacement of a structure to be measured, unlike other traditional sensors (i.e. accelerometers and strain gages). However, past research efforts have shown GPS to suffer from the effects of multipath interference, greatly reducing its accuracy in urban areas. In this study, a testing program was developed in which a controlled multipath source was introduced into a GPS network to allow for the characterization and removal of this phenomenon. In addition, the GPS performance was benchmarked against two more widely accepted sensor technologies: a terrestrial positioning system (TPS) and an accelerometer, to demonstrate its utility for monitoring CIS.
Health monitoring is becoming an increasingly valuable tool for assessment of aging infrastructure in urban zones. For such applications, Global Positioning Systems (GPS) present a promising monitoring technique-one that is able to capture the total displacements of a structure. However, due to the relative infancy of this technology, there are still issues to be resolved, including the characterization and removal of multipath effects. This paper discusses the manifestation and removal of multipath errors by examining the full-scale response of a tall building to demonstrate the accuracy of high precision GPS in comparison with traditional sensors like accelerometers.
This study chronicles the first stages of an ongoing research initiative to monitor several tall buildings in Chicago under the action of wind. From these measurements, comparisons with predicted analytical values and wind tunnel tests will provide valuable insights into the accuracy of current design strategies, highlighting areas for improvement to advance the state-of-the-art in tall building design. This paper overviews the entire project with detailed treatment of the current phase: the instrumentation of the four buildings.