System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Virus coevolution partheno-genetic algorithms for optimal sensor placement
Advanced Engineering Informatics
Configuration of measurement systems using Shannon's entropy function
Computers and Structures
Sensor data driven proactive management of infrastructure systems
EG-ICE'06 Proceedings of the 13th international conference on Intelligent Computing in Engineering and Architecture
Methodologies for model-free data interpretation of civil engineering structures
Computers and Structures
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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.