Measuring sub-mm structural displacements using QDaedalus: a digital clip-on measuring system developed for total stations

The monitoring of rigid structures of modal frequencies greater than 5 Hz and sub-mm displacement is mainly based so far on relative quantities from accelerometers, strain gauges etc. Additionally geodetic techniques such as GPS and Robotic Total Stations (RTS) are constrained by their low accuracy (few mm) and their low sampling rates. In this study the application of QDaedalus is presented, which constitutes a measuring system developed at the Geodesy and Geodynamics Lab, ETH Zurich and consists of a small CCD camera and Total Station, for the monitoring of the oscillations of a rigid structure. In collaboration with the Institute of Structural Engineering of ETH Zurich and EMPA, the QDaedalus system was used for monitoring of the sub-mm displacement of a rigid prototype beam and the estimation of its modal frequencies up to 30 Hz. The results of the QDaedalus data analysis were compared to those of accelerometers and proved to hold sufficient accuracy and suitably supplementing the existing monitoring techniques

Uncertain natural frequency analysis of composite plates including effect of noise – A polynomial neural network approach

Acknowledgement SN and SS gratefully acknowledge the financial support from Lloyd’s Register Foundation Centre during this work.Peer reviewedPostprin

Multivariate ARMA Based Modal Identification of a Time-Varying Beam

The present paper addresses the problem of modal identification of time-varying systems. The identification is based on a multivariate autoregressive moving-average model in which the time variability of the system is caught using a basis functions approach. In this approach, the time-varying regressive coefficients in the model are expended in the chosen basis functions and only the projection coefficients have to be identified. In that way, the initial time-varying problem then becomes a time-invariant one that can be solved. Because a multivariate model is used, in addition to the time-varying poles, the time-varying mode shapes may be identified too. The method is first presented and then applied on an experimental demonstration structure. The experimental structure consists of a supported beam on which a mass is travelling. The mass is chosen sufficiently large to have a significant influence on the dynamics of the primary system. This kind of problem is a classical example commonly used by many authors to test time-varying identification methods

Εξελιγμένες και πλήρεις μέθοδοι συναρτησιακών χρονικά μεταβαλλόμενων μοντέλων αυτοπαλινδρόμησης και κινητού μέσου όρου (FS-TARMA) για την δυναμική αναγνώριση και διάγνωση βλαβών σε μη-στάσιμα στοχαστικά συστήματα κατασκευών

Non-stationary signals, that is signals with time-varying (TV) statistical properties, are commonly encountered in engineering practice. The vibration responses of structures, such as traffic-excited bridges, robotic devices, rotating machinery, and so on, constitute typical examples of non-stationary signals. Structures characterized by properties that vary with time are generally referred as TV structures and their vibration-based identification under normal operating conditions is a significant and challenging problem. An important class of parametric methods for the solution of this problem is based on Functional Series Time-dependent AutoRegressive Moving Average (FS-TARMA) models. These models have parameters that explicitly depend on time, with the dependence described by deterministic functions belonging to specific functional sub-spaces. The focus of the present thesis is on the development of complete and advanced FS-TARMA methods that will offer important improvements in overcoming drawbacks of existent methods and will further foster practical use and application of FS-TARMA models in non-stationary vibration analysis. The specific objectives of the thesis are: a) The introduction of a novel class of Adaptable FS-TARMA (AFS-TARMA) models and the development of a method for their effective identification. AFS-TARMA models are adaptable in the sense that they are not based on basis functions of a fixed form, but instead, they use basis functions with a-priori unknown properties that may adapt to the specific random signal characteristics. b) The postulation of a vector FS-TARMA method for output-only structural identification and the development of effective tools for both model parameter estimation and model structure selection. c) The introduction of a statistical method for vibration-based fault diagnosis in TV structures. d) The presentation of a thorough review on FS-TARMA models covering both theoretical and practical aspects of the model parameter estimation and structure selection problems with special emphasis being placed on promising recent methods. The methods that are developed in each chapter of this thesis are validated through their application in both numerical and experimental case studies and comparisons with currently available non-stationary signal identification methods. The results of the study demonstrate the new methods' applicability, effectiveness, and high potential for parsimonious and accurate identification and dynamic analysis of TV structures.Μη-στάσιμα σήματα, δηλαδή σήματα με χρονικά μεταβαλλόμενες (ΧΜ) στατιστικές ιδιότητες, απαντώνται συχνά στην επιστήμη του μηχανικού. Τυπικά παραδείγματα αποτελούν οι ταλαντωτικές αποκρίσεις κατασκευών, όπως γέφυρες με κινούμενα οχήματα, ρομποτικές διατάξεις, περιστρεφόμενες μηχανές και άλλες. Κατασκευές που χαρακτηρίζονται από ιδιότητες οι οποίες μεταβάλλονται με τον χρόνο αναφέρονται ως ΧΜ κατασκευές και η δυναμική αναγνώριση και ανάλυση τους επί τη βάση ταλαντωτικών σημάτων απόκρισης αποτελεί σημαντικό και ταυτόχρονα δύσκολο πρόβλημα. Μια σημαντική τάξη παραμετρικών μεθόδων για την επίλυση αυτού του προβλήματος βασίζεται στα συναρτησιακά χρονικά μεταβαλλόμενα μοντέλα αυτοπαλινδρόμησης κινητού μέσου όρου (FS-TARMA, Functional Series Time-Dependent Auto-Regressive Moving Average). Τα μοντέλα αυτά χαρακτηρίζονται απο ΧΜ παραμέτρους οι οποίες ακολουθούν καθοριστικό πρότυπο και κατά συνέπεια μπορούν να προβληθούν σε κατάλληλα επιλεγμένους συναρτησιακούς υποχώρους. Ως βασικός στόχος της παρούσας διατριβής ορίζεται η ανάπτυξη εξελιγμένων μεθόδων μοντελοποίησης FS-TARMA οι οποίες θα προσφέρουν σημαντικές βελτιώσεις στις υπάρχουσες προσεγγίσεις και θα βοηθήσουν στην αντιμετώπιση πρακτικών προβλημάτων που σχετίζονται τόσο με την αναγνώριση των δυναμικών χαρακτηριστικών όσο και την διάγνωση βλαβών σε ΧΜ κατασκευές. Οι συγκεκριμένοι στόχοι της διατριβής μπορούν να περιγραφούν ως ακολούθως: α) Εισαγωγή καινοτόμων προσαρμόσιμων μοντέλων FS-TARMA και ανάπτυξη κατάλληλης μεθόδου για την αποτελεσματική εκτίμηση τους. Τα νέα μοντέλα είναι προσαρμόσιμα υπό την έννοια ότι δεν βασίζονται σε προκαθορισμένες συναρτήσεις βάσης, αλλά αντιθέτως χρησιμοποιούν συναρτήσεις βάσης με εκ των προτέρων άγνωστες ιδιότητες οι οποίες μπορούν να προσαρμοστούν στα χαρακτηριστικά συγκεκριμένου σήματος. β) Ανάπτυξη διανυσματικής μεθόδου εκτίμησης μοντέλων FS-TARMA για την αναγνώριση κατασκευών μέσα από διανυσματικά σήματα ταλαντωτικής απόκρισης. Ανάπτυξη αποδοτικών εργαλείων τόσο για το πρόβλημα εκτίμησης των παραμέτρων όσο και της επιλογής της δομής του μοντέλου. γ) Εισαγωγή στατιστικής μεθόδου για την διάγνωση βλαβών σε ΧΜ κατασκευές μέσω μοντέλων FS-TAR. δ) Παρουσίαση μιας διεξοδικής επισκόπησης των μοντέλων FS-TARMA η οποία καλύπτει τόσο θεωρητικά όσο και πρακτικά ζητήματα των προβλημάτων εκτίμησης των παραμέτρων και επιλογής της δομής των μοντέλων. Η αποτελεσματικότητα των μοντέλων και των μεθόδων που αναπτύσσονται σε κάθε κεφάλαιο αυτής της διατριβής διερευνάται µέσω της εφαρµογής τους τόσο σε αριθµητικές όσο και πειραµατικές µελέτες και συγκρίσεις µε υπάρχουσες µη-στάσιµες µεθόδους αναγνώρισης σηµάτων. Τα αποτελέσματα της εργασίας αυτής επιδεικνύουν την ικανότητα των νέων μοντέλων να παρέχουν εξαιρετικά ακριβείς αναπαραστάσεις ΧΜ κατασκευών κατάλληλων τόσο για την δυναμική ανάλυση όσο και για την διάγνωση βλαβών σε αυτές

Data-driven polynomial chaos basis estimation

A non-intrusive uncertainty quantification scheme based on Polynomial Chaos (PC) basis constructed from available data is introduced. The method uses properly parametrized basis functions in order to let them adapt to the given input-output data instead of predefining them based on the probability density function of the uncertain input variable. Model parameter estimation is effectively dealt with through a Separable Non-linear Least Squares (SNLS) procedure that allows the simultaneous estimation of both the PC basis and the corresponding coefficients of projection. Method’s effectiveness is demonstrated through its application to the uncertainty propagation modelling in two examples: a nonlinear differential equation with uncertain initial conditions and a nonlinear single degree-of-freedom system with an uncertain parameter. Comparisons with classical PC expansion modelling based on the Wiener-Askey scheme are used to illustrate the method’s performance and potential advantages.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.FacultyResearche

Structural Identification and Monitoring based on Uncertain/Limited Information

The goal of the present study is to propose a structural identification framework able to exploit both vibrational response and operational condition information in extracting structural models, able to represent the systemspecific structural behavior in its complete operational spectrum. In doing so, a scheme need be derived for the extraction of salient features, which are indicative of structural condition. Such a scheme should account for variations attributed to operational effects, such as environmental and operational load variations, and which likely lie within regular structural condition bounds, versus variations which indicate short- or long-term damage effects. The latter may be achieved via coupling of sparse, yet diverse, monitoring information with appropriate stochastic tools, able to infer the underlying dependences between the monitored input and output data. This in turn allows for extraction of quantities, or features, relating to structural condition, which may further be utilized as performance indicators. The computational tool developed herein for realizing such a framework, termed the PCE-ICA scheme, is based on the use of Polynomial Chaos Expansion (PCE) tool, along with an Independent Component Analysis (ICA) algorithm. The benefits of additionally fusing a data-driven system model will further be discussed for the case of complex structural response. The method is assessed via implementation on field data acquired from diverse structural systems, namely a benchmark bridge case study and a wind turbine tower structure, revealing a robust condition assessment tool

Structural Identification and Monitoring based on Uncertain/Limited Information

The goal of the present study is to propose a structural identification framework able to exploit both vibrational response and operational condition information in extracting structural models, able to represent the systemspecific structural behavior in its complete operational spectrum. In doing so, a scheme need be derived for the extraction of salient features, which are indicative of structural condition. Such a scheme should account for variations attributed to operational effects, such as environmental and operational load variations, and which likely lie within regular structural condition bounds, versus variations which indicate short- or long-term damage effects. The latter may be achieved via coupling of sparse, yet diverse, monitoring information with appropriate stochastic tools, able to infer the underlying dependences between the monitored input and output data. This in turn allows for extraction of quantities, or features, relating to structural condition, which may further be utilized as performance indicators. The computational tool developed herein for realizing such a framework, termed the PCE-ICA scheme, is based on the use of Polynomial Chaos Expansion (PCE) tool, along with an Independent Component Analysis (ICA) algorithm. The benefits of additionally fusing a data-driven system model will further be discussed for the case of complex structural response. The method is assessed via implementation on field data acquired from diverse structural systems, namely a benchmark bridge case study and a wind turbine tower structure, revealing a robust condition assessment tool