Rational Design of Measurement Systems using Information Science

Advanced Engineering Informatics

Smith

2009

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: Iterative Model Filteringmentioning

confidence: 99%

Configuring and enhancing measurement systems for damage identification

Advanced Engineering Informatics

Smith

2009

“…Measurements could be from the initial measurement system or from a sensor that was added during the previous iteration. A method of designing initial measurement systems that is suitable for identification using multiple models is given in Saitta et al (2006).…”

Section: Methodsmentioning

confidence: 99%

“…Recently, sensor placement strategies regarding multiple models have been studied (Robert-Nicoud et al, 2005b,a). In Saitta et al (2006), greedy and global search approaches have been compared for initial sensor placement. Although successful in some situations, global search is not ideal for iterative sensor addition due to higher computation costs.…”

Section: Introductionmentioning

confidence: 99%

Improving System Identification Using Clustering

Saitta

Raphael

et al. 2008

J. Comput. Civ. Eng.

Self Cite

System identification involves identification of a behavioral model that best explains the measured behavior of a structure. This research uses a strategy of generation and iterative filtering of multiple candidate models for system identification. The task of model filtering is supported by measurement cycles. During each measurement cycle, the location for subsequent measurement can be chosen using the predictions of current candidate models. In this paper, data mining techniques are proposed to support such measurement-interpretation cycles. Candidate models, representing possible states of a structure, are clustered using a technique that combines principal component analysis and K-means clustering. Representative models of each cluster are used to place sensors for subsequent measurement on the basis of the entropy of their predictions. Models are filtered from candidate model sets using new measurements. Results show that clustering is necessary to identify the different groups of candidate models. The entropy of predictions is found to be a valid stopping criterion for iterative sensor addition. While measurement-interpretation cycles can lead to a unique model for structures with low levels of complexity, engineers may be left with large numbers of models for structures with higher levels of uncertainty. In those situations, clustering is a powerful tool to classify models and thus provide much fewer representative models to engineers for further decision making.

“…Since system identification is an inverse problem and errors are involved in both measurement and modeling, many models may be able to explain the same measurement (Smith, 2005). Therefore it is of interest to configure measurement systems such that maximum separation between candidate models can be achieved (Saitta et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Measurement System Design Using Damage Scenarios

Computing in Civil Engineering (2007)

Ravindran

Saitta

et al. 2007

Self Cite

This paper presents a measurement system design methodology that incorporates damage scenarios. One of the goals of a measurement system is to detect damage for safety and for management of maintenance activities. This study builds upon previous research at EPFL in the area of model based diagnosis. An existing bridge in Switzerland is used to illustrate how damage scenarios are identified and a population of models are generated. A greedy algorithm is used to place sensors such that there is maximum separation between the predictions of the models in the population.