Piotr Omenzetter

Bio: Piotr Omenzetter is an academic researcher from University of Aberdeen. The author has contributed to research in topics: Structural health monitoring & Bridge (interpersonal). The author has an hindex of 19, co-authored 127 publications receiving 1664 citations. Previous affiliations of Piotr Omenzetter include University of Auckland & Nanyang Technological University.

Assessment of highway bridge upgrading by dynamic testing and finite-element model updating

Abstract: The Land Transport Authority of Singapore has a continuing program of highway bridge upgrading for refurbishing and strengthening bridges to allow for increasing vehicle traffic and increasing axle loads. One subject of this program has been a short-span bridge taking a busy main road across a coastal inlet near a major port facility. Experiment-based structural assessments of the bridge were conducted before and after upgrading works including strengthening. Each assessment exercise comprised three separate components: ~1! a strain and acceleration monitoring exercise lasting approximately one month; ~2! a full-scale dynamic test carried out in a single day without closing the bridge; and ~3! a finite-element model updating exercise to identify structural parameters and mechanisms. This paper presents the dynamic testing and the modal analysis used to identify the vibration properties and the quantification of the effectiveness of the upgrading through the subsequent model updating. Before and after upgrade, similar sets of vibration modes were identified, resem- bling those of an orthotropic plate with relatively weak transverse bending stiffness. Conversion of bearings from nominal simple supports to nominal full fixity was shown via model updating to be the principal cause of natural frequency increases of up to 50%. The utility of the combined experimental and analytical process in direct identification of structural properties has been proven, and the procedure can be applied to other structures and their capacity assessments.

Application of time series analysis for bridge monitoring

Abstract: Despite recent considerable advances in structural health monitoring (SHM) of civil infrastructure, converting large amounts of data from SHM systems into usable information and knowledge remains a great challenge. This paper addresses the problem through the analysis of time histories of static strain data recorded by an SHM system installed in a major bridge structure and operating continuously for a long time. The reported study formulates a vector seasonal autoregressive integrated moving average (ARIMA) model for the recorded strain signals. The coefficients of the ARIMA model are allowed to vary with time and are identified using an adaptive Kalman filter. The proposed method has been used for analysis of the signals recorded during the construction and service life of the bridge. By observing various changes in the ARIMA model coefficients, unusual events as well as structural change or damage sustained by the structure can be revealed.

A spectral density approach for modelling continuous vertical forces on pedestrian structures due to walking

Abstract: Existing walking models used for vibration serviceability assessment of structures carrying pedestrians are typically based on measurements of single footfalls replicated at precise intervals. This...

Particle Swarm Optimization with Sequential Niche Technique for Dynamic Finite Element Model Updating

Abstract: Due to uncertainties associated with material properties, structural geometry, boundary conditions, and connectivity of structural parts as well as inherent simplifying assumptions in the development of finite element (FE) models, actual behavior of structures often differs from model predictions FE model updating comprises a multitude of techniques that systematically calibrate FE models in order to match experimental results Updating of structural models can be posed as an optimization problem where model parameters that minimize the errors between the responses of the model and actual structure are sought However, due to limited number of experimental responses and measurement errors, the optimization problem may have multiple admissible solutions in the search domain Global optimization algorithms (GOAs) are useful and efficient tools in such situations as they try to find the globally optimal solution out of many possible local minima, but are not totally immune to missing the right minimum in complex problems such as those encountered in updating A methodology based on particle swarm optimization (PSO), a GOA, with sequential niche technique (SNT) for FE model updating is proposed and explored in this article The combination of PSO and SNT enables a systematic search for multiple minima and considerably increases the confidence in finding the global minimum The method is applied to FE model updating of a pedestrian cable-stayed bridge using modal data from full-scale dynamic testing

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Forecasting, Structural Time Series Models and the Kalman Filter

Abstract: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Structural health monitoring of civil infrastructure

Abstract: Structural health monitoring (SHM) is a term increasingly used in the last decade to describe a range of systems implemented on full-scale civil infrastructures and whose purposes are to assist and inform operators about continued 'fitness for purpose' of structures under gradual or sudden changes to their state, to learn about either or both of the load and response mechanisms. Arguably, various forms of SHM have been employed in civil infrastructure for at least half a century, but it is only in the last decade or two that computer-based systems are being designed for the purpose of assisting owners/operators of ageing infrastructure with timely information for their continued safe and economic operation. This paper describes the motivations for and recent history of SHM applications to various forms of civil infrastructure and provides case studies on specific types of structure. It ends with a discussion of the present state-of-the-art and future developments in terms of instrumentation, data acquisition, communication systems and data mining and presentation procedures for diagnosis of infrastructural 'health'.

Random Data Analysis And Measurement Procedures

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