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Sensors 2017, 17(12), 2904;

Optimal Multi-Type Sensor Placement for Structural Identification by Static-Load Testing

ETH Zurich, Future Cities Laboratory, Singapore-ETH Centre, 1 CREATE Way, CREATE Tower, Singapore 138602, Singapore
Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology (EPFL), CH-1015 Lausanne, Switzerland
Author to whom correspondence should be addressed.
Received: 16 October 2017 / Revised: 24 November 2017 / Accepted: 5 December 2017 / Published: 14 December 2017
(This article belongs to the Special Issue Sensors and Sensor Networks for Structural Health Monitoring)
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Assessing ageing infrastructure is a critical challenge for civil engineers due to the difficulty in the estimation and integration of uncertainties in structural models. Field measurements are increasingly used to improve knowledge of the real behavior of a structure; this activity is called structural identification. Error-domain model falsification (EDMF) is an easy-to-use model-based structural-identification methodology which robustly accommodates systematic uncertainties originating from sources such as boundary conditions, numerical modelling and model fidelity, as well as aleatory uncertainties from sources such as measurement error and material parameter-value estimations. In most practical applications of structural identification, sensors are placed using engineering judgment and experience. However, since sensor placement is fundamental to the success of structural identification, a more rational and systematic method is justified. This study presents a measurement system design methodology to identify the best sensor locations and sensor types using information from static-load tests. More specifically, three static-load tests were studied for the sensor system design using three types of sensors for a performance evaluation of a full-scale bridge in Singapore. Several sensor placement strategies are compared using joint entropy as an information-gain metric. A modified version of the hierarchical algorithm for sensor placement is proposed to take into account mutual information between load tests. It is shown that a carefully-configured measurement strategy that includes multiple sensor types and several load tests maximizes information gain. View Full-Text
Keywords: structural identification; measurement systems; sensors; model falsification; joint entropy; uncertainties; load tests structural identification; measurement systems; sensors; model falsification; joint entropy; uncertainties; load tests

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Bertola, N.J.; Papadopoulou, M.; Vernay, D.; Smith, I.F.C. Optimal Multi-Type Sensor Placement for Structural Identification by Static-Load Testing. Sensors 2017, 17, 2904.

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