This paper presents the development of a continuous dynamic monitoring System and its applications to different structures, with the purpose of understanding structural real behaviours under operational conditions and detecting early structural modifications. The first part of paper introduces a complete continuous dynamic System, consists of signal acquisition and communication, automated signal processing and management, investigation of the interaction between structures and its environmental/operational conditions, feature extraction and detection of structural modification.
The rest of paper describes the applications of continuous dynamic monitoring System to different structures such as a wind turbine System and a highway bridge.
The implementation of a continuous dynamic monitoring System in Pedro e Ines footbridge at Coimbra, Portugal, operating since June 2007, enabled to detect different environmental/operational effects on the modal properties, based on appropriate processing of monitoring data collected by a set of accelerometers and thermal sensors.
In order to remove or mitigate such environmental/operational effects with the purpose of damage detection, two different Statistical methods have been adopted, One of them consists in the multiple linear regression (MLR) by performing correlation analysis between measured modal properties and environmental/operational variables. Another is based on the identification of the linear subspace within the modal properties by applying principal component regression (PCR) without using measured values of environmental and operational variables.
This paper presents a comparison ofthe performance of these two alternative approaches on the basis of continuous monitoring data acquired front the instrumented Pedro e Ines footbridge.
This paper presents the continuous dynamic measurements of an aging prestressed box-beam bridge during the period from 2000 to 2013, which consists of dynamic properties and environmental/operational factors, as well as the Variation of the extracted health features after removing the environmental/operational effects. Firstly, the variations of frequency estimates, temperatures and traffic loads from 2000 to 2013 are described. Subsequently, correlation analysis between them indicates the nonlinear influences of the temperature on the frequency estimates. The Multiple Linear Regression (MLR) method is used to remove the environmental/operational effects and the health index is proposed by performing the Novelty Detection analysis of the residual errors. Finally, the variations of the health index may indicate clear structural modifications that can be also characterized by the long term monitoring of the dynamic strain in the prestressed cable.