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- Structural health monitoring (2) (entfernen)
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.
The Westend Bridge is located on the A100 Highway in Berlin. An integrated continuous dynamic monitoring system, composed of 20 velocity sensors, 5 temperature sensors, 3 strain gauges, 1 crack sensor and 2 inclination sensors, was implemented by the Federal Institute for Materials Research and Testing in 2000. The system runs continuously with occasional intermittence and led to a huge amount of data over a 14-year span. In this article, variations of the strain, crack and inclination measurements during the last 14 years are presented. It is noted that the observed crack and inclination of the bridge are strongly influenced by seasonal temperature variation. It further induces change in the relationship between the strains measured in both concrete and prestressed tendon. Application of k-means cluster Analysis technique in both the crack and strain measurements can partition them into different seasonal phases by identifying ‘turning points’ that indicate annual periodical bridge change. In the period of these two ‘turning points’, a strong linear relation of the strains in two materials is observed. In the rest of the year, a nonlinear relationship between the strains recorded in both the concrete and the prestressed tendon is noted. The possible reason is the additional thermal load due to the change in temperature difference between the bridge’s surface and soffit. Finally, a health index in a Framework of regression model and process control theory is proposed by investigating the linear relationship between the strains in concrete and prestressed tendon. The tendency of the health index in the 14 years may suggest the long-term bridge change during that time frame.