Claude Leyder

Optimal sensor placement for the modal identification of an innovative timber structure

This paper aims at determining an optimal grid of acceleration sensors for the accurate estimation of modal data from an innovative timber-hybrid structure. As the knowledge on the full-scale behaviour of such structures is limited thus far, an extensive identification campaign on an innovative timber building, the ETH House of Natural Resources, is currently being carried out at ETH Zurich. In conjunction with this campaign an optimal placement for modal identification sensors is undertaken, in order to extract the maximum possible information from a minimal number of sensors. The entire structure is modelled in SAP2000 and the modal information (frequencies and mode shapes) is extracted from the software. The obtained mode shapes are then utilized as the input to the sensor placement problem. Three widely accepted sensor placement methods are implemented and assessed, namely, the effective independence method (EFI), the driving point residue EFI method (EFI-DPR) and the maximum kinetic energy method (MKE). The optimization algorithms identify the most relevant degrees of freedom, which should be monitored during the testing campaign.

Comparison of optimal sensor placement algorithms via implementation on an innovative timber structure

This research work aims at deriving an optimal sensor configuration for the modal identification of an innovative timber structure. The goal is to extract the maximum possible information from the structure, while minimizing the amount of deployed sensors. Several different optimal placement methods are implemented, including the effective independence method (EFI), the modal kinetic energy method (MKE) and the information entropy method (IEI). Additionally, a modified version of the IEI method, incorporating a prediction error correlation term is introduced. This addition alleviates sensor clustering around a single location, which often occurs if the modal input data is generated from a finite element model of a dense mesh of possible sensor positions. The study shows, that to obtain an optimal sensor setup, prediction error correlation effects should be considered in order to avoid sensor clustering, and the multiaxiality of sensors should be taken into account during the optimization process.

Optimal sensor placement methods and metrics – comparison and implementation on a timber frame structure

Abstract The current work aims at determining optimal sensor configurations for the modal identification of a post-tensioned timber frame structure. The objective is to maximise the information gained from the structural testing, while keeping the number of necessary sensors to a minimum. Three different and widely used optimal sensor placement (OSP) methods are investigated and evaluated based on appropriate metrics, namely the effective independence method, the modal kinetic energy method and the information entropy (IE) method. An enhanced IE variant is adopted, which resolves the problem of close positioning of sensors, which forms a common issue when sensor positions are selected from a dense grid. In terms of the adopted metrics, three different options are investigated, namely, the information entropy index, the Modal Assurance Criterion and a newly introduced metric, the relative dispersion index. For the quantification of trade-offs among the selected metrics a Pareto Front scheme is realised. The study indicates that the evaluation of different OSP configurations is strongly dependent on the employed metric. It is therefore of vital importance to select the appropriate metric when determining optimal sensor positions.