Measurement System Design Using Damage Scenarios

Advanced Engineering Informatics

2009

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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.

Section: Introductionmentioning

confidence: 99%

Configuring and enhancing measurement systems for damage identification

Advanced Engineering Informatics

2009

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“…The set of model parameters may consist of quantities such as elastic constant, bearing device stiffness and section thickness. Based on this a-priori model set, sensors are placed at location given by an algorithm previously developed at EPFL [1][2][3]. The objective of this process is to find sensor types and locations that will separate (i.e.…”

Section: System Identification Using Multi-model Approachmentioning

confidence: 99%

Structural Identification to Improve Bridge Management

Goulet

IABSE Symposium, Bangkok 2009: Sustainable Infrastructure - Environment Friendly, Safe and Resource Efficient

2009

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This paper presents results from static loads tests performed on the new Langensand Bridge built in Switzerland. A systematic study of over 1000 models subjected to three load cases identifies a set of 11 candidate models through static measurements. Predictions using the set of candidate models are homogenous and show an averaged discrepancy ranging of 4 to 7% from the displacement measurements. All candidate models have values for material proprieties that are close to expected values. This finding confirms that the behaviour of the structure conforms to the design expectations. Comparing the candidate model set to a design model that takes into account only main structural elements shows that the structure has approximately 30% reserve capacity with respect to a typical deflection risk scenario according to Swiss codes. The population of candidate models may be used to understand and predict the behaviour of the full bridge prior to its completion.

Considering Sensor Characteristics during Measurement-System Design for Structural System Identification

Goulet

Computing in Civil Engineering (2009)

2009

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This paper presents a method for measurement-system design through criteria related to model based structural identification. Using a multi-model approach and results from previous research carried out at EPFL, an improved algorithm is proposed. The algorithm accounts for various types of sensors having different accuracies and taking different kinds of measurements. The algorithm selects sensor types and locations that minimise the number of non-identified candidate models. The results show that the approach provides an alternative to selecting and placing sensors using engineering experience alone, and that a scientific approach based on sensor characteristics and modelling error is feasible. A single span composite bridge is used to illustrate the algorithm. It is shown that adding more than 9 sensors, from a possible set of 34, will not provide further useful information for structural identification.