Wei-a Hu

LabVIEW toolkits for output-only modal identification and long-term dynamic structural monitoring

This paper describes the development of computational tools for modal identification and long term dynamic monitoring in LabVIEW environment. These tools mainly consist of two individual toolkits for structural modal identification (SMI) and continuous monitoring (CSMI), respectively. The SMI toolkit implements the frequency domain Peak-Picking (PP) and Enhanced Frequency Domain Decomposition (EFDD) method, as well as the time domain Stochastic Subspace Identification (SSI) techniques. Based on this toolkit, the user can easily develop the whole process of structural modal identification by simply pushing buttons. The CSMI toolkit was conceived for continuous dynamic monitoring excluding manual interaction. It automatically searches the latest output measurements, detects maximum vibration amplitudes and makes statistical treatment of acceleration time series, generates waterfall plots for depicting the frequency component distribution and identifies modal parameters based on automated EFDD technique. The application of these tools is briefly described based on experimental data collected at Pinhão bridge and Coimbra footbridge.

Continuous dynamic monitoring of a prestressed concrete bridge based on strain, inclination and crack measurements over a 14-year span:

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, 2 inclination sensors and 1 crack sensor was implemented by the Federal Institute for Materials Research and Testing (BAM) in 2000. The system runs continuously with occasional intermittence and leads 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.

Dynamic monitoring of civil engineering structures

The paper describes two dynamic monitoring systems installed in civil engineering structures, a footbridge and a suspended stadium roof. These systems are used for characterization of environmental and operational effects and, in the limit, for the early detection of damage. Some examples of collected data and results are presented.

Dynamic monitoring of lively footbridges

The demo will focus on two dynamic monitoring systems installed in footbridges with the purpose of characterizing the dynamic behavior and response of these structures and possibly detecting damage at early stages.