Configuring and enhancing measurement systems for damage identification

Engineers often decide to measure structures upon signs of damage to determine its extent and its location. Measurement locations, sensor types and numbers of sensors are selected based on judgment and experience. Rational and systematic methods for evaluating structural performance can help make better decisions. This paper proposes strategies for supporting two measurement tasks related to structural health monitoring – (1) installing an initial measurement system and (2) enhancing measurement systems for subsequent measurements once data interpretation has occurred. The strategies are based on previous research into system identification using multiple models. A global optimization approach is used to design the initial measurement system. Then a greedy strategy is used to select measurement locations with maximum entropy among candidate model predictions. Two bridges are used to illustrate the proposed methodology. First, a railway truss bridge in Zangenberg, Germany is examined. For illustration purposes, the model space is reduced by assuming only a few types of possible damage in the truss bridge. The approach is then applied to the Schwandbach bridge in Switzerland, where a broad set of damage scenarios is evaluated. For the truss bridge, the approach correctly identifies the damage that represents the behaviour of the structure. For the Schwandbach bridge, the approach is able to significantly reduce the number of candidate models. Values of candidate model parameters are also useful for planning inspection and eventual repair

Structural identification through continuous monitoring: data cleansing using temperature variations

The aim of structural performance monitoring is to infer the state of a structure from measurements and thereby support decisions related to structural management. Complex structures may be equipped with hundreds of sensors that measure quantities such as temperature, acceleration and strain. However, meaningful interpretation of data collected from continuous monitoring remains a challenge. MPCA (Moving principal component analysis) is a model-free data interpretation method which compares characteristics of a moving window of measurements against those derived from a reference period. This paper explores a data cleansing approach to improve the performance of MPCA. The approach uses a smoothing procedure or a low-pass filter (moving average) to exclude the effects of seasonal temperature variations. Consequently MPCA can use a smaller moving window and therefore detect anomalies more rapidly. Measurements from a numerical model and a prestressed beam are used to illustrate the approach. Results show that removal of seasonal temperature effects can improve the performance of MPCA. However, improvement may not be significant and there remains a trade off when choosing the window size. A small window increases the risk of false-positives while a large window increases the time to detect damage

Dynamic behavior and vibration control of a tensegrity structure

Tensegrities are lightweight space reticulated structures composed of cables and struts. Stability is provided by the self-stress state between tensioned and compressed elements. Tensegrity systems have in general low structural damping, leading to challenges with respect to dynamic loading. This paper describes dynamic behavior and vibration control of a full-scale active tensegrity structure. Laboratory testing and numerical simulations confirmed that control of the self-stress influences the dynamic behavior. A multi-objective vibration control strategy is proposed. Vibration control is carried out by modifying the self-stress level of the structure through small movement of active struts in order to shift the natural frequencies away from excitation. The PGSL stochastic search algorithm successfully identifies good control commands enabling reduction of structural response to acceptable levels at minimum control cost. (C) 2010 Elsevier Ltd. All rights reserved