Original Article Proc IMechE Part I: J Systems and Control Engineering 227(7) 577–587 Ó IMechE 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0959651813493408 pii.sagepub.com Modal tests on buildings: correlating large amounts of acquisitions with different space–time collocations Rinaldo Garziera, Luca Collini and Elisabetta Manconi Abstract This article deals with a method for the time and space superposition of data acquired in the dynamical testing of large structures. The method permits two procedures comprising the integration of sets of measurements taken at different times and the integration of sets of measurements taken at different places. The latter is avery useful feature when deal- ing with huge structures, such as big buildings comprising a number of different architectural details. The validation of this method is a case study consisting of many dynamical tests performed on an ancient castle. Keywords Modal test, data acquisition, time and space superposition, health monitoring of large structures, finite element analysis Date received: 4 February 2013; accepted: 21 May 2013 Introduction Generally, the acquisition of experimental, technical and scientific accelerometric data is a consolidated prac- tice. In most cases, quite a few acquisition points are sufficient to realise good modal analyses, and test point measures are easily collected and elaborated. Other common situations are those in which test points are predefined measurement locations, often built with the apparatus to be monitored. 1 In this latter situation, we mostly refer to large turbomachinery plants (e.g. power plants) where continuous vibration monitoring is per- formed in order to obtain real-time diagnostic data. 2 For these cases, the set of test points and the acquisition apparatus are very finely tuned with each other. In these, let us call them ‘regular circumstances’ when a vibration test is performed, it can be studied promptly in terms of fast Fourier transform (FFT) or frequency response function (FRF) and subsequent modal recon- struction when appropriate. In fact, a researcher is then scarcely motivated to perform data superposition before analysis. On the contrary, when more than one set of acquisition data is present, it is a quite common practice to mediate over already ‘cooked’ data, as in an FFT. Accelerometric data are often acquired for detecting damage and monitoring the health of structures. Damage detection and the health monitoring of struc- tures can be investigated by analysing and comparing acceleration time histories. 3 In this case, a data fusion technique can be applied with the purpose of integrat- ing data from a multitude of sensors to make a more confident damage detection decision than is possible with any one sensor alone. In many cases, data fusion is performed in an unsophisticated manner such as when one examines relative information between vari- ous sensors to obtain mode shapes. At other times, complex analyses of information from sensor arrays such as those provided by artificial neural networks 4 are used in the data fusion process. Locations of dam- age can also be predicted by investigating qualitative and quantitative changes in mode shapes and natural frequencies, which are obtained from accelerometers placed on the structure of interest. Several authors have performed studies on damage detection and health monitoring of structures, and much original and inter- esting work has been produced. Literature on this sub- ject is vast, and hence, for the sake of brevity, just a few relevant works concerning civil structures are cited here. In the study by Krishnamurthy et al., 5 a rigorous theoretical framework is presented for the analysis of the effects of time delays in the system response Department of Industrial Engineering, University of Parma, Parma, Italy Corresponding author: Luca Collini, Department of Industrial Engineering, University of Parma, V.le G.P. Usberti 181/A, 43100 Parma, Italy. Email: lucaferdinando.collini@gmail.com