Papers by Author: Piotr Omenzetter

Abstract: Because of the critical importance of bridges in land transport networks and broader economy, an increasing interest in permanent observation of their dynamic behavior under traffic, seismic and other live loads has been observed during the past decade. In addition, recent technological advances have made the installation and operation of permanent dynamic monitoring systems much more practical and economical. A multi-channel dynamic monitoring system is being installed in the 12 span pre-cast, post-tensioned Newmarket Viaduct, recently built using the balanced cantilever method and situated in Auckland, New Zealand. This paper first describes the preliminary studies including extensive one-off ambient vibration tests using wireless sensors that provided important information for the design of the monitoring system shortly after construction of the bridge. Then the designed monitoring system is characterized and proposed research that will be undertaken using the monitoring data is outlined.

Abstract: This paper describes the design of a system to monitor floor vibrations in an office building and an analysis of several months worth of collected data. Floors of modern office buildings are prone to occupant-induced vibrations. The contributing factors include long spans, slender and flexible designs, use of lightweight materials and low damping. As a result, resonant frequencies often fall in the range easily excited by normal footfall loading, creating potential serviceability problems due to undesirable levels of vibrations. This study investigates in-situ performance of a non-composite timber-concrete floor located in a recently constructed innovative multi-storey office building. The floor monitoring system consists of several displacement transducers to measure long-term deformations due to timber and concrete creep and three accelerometers to measure responses to walking forces, the latter being the focus of this paper. Floor response is typically complex and multimodal and the optimal accelerometer locations were decided with the help of the effective independence-driving point residue (EfI-DPR) technique. A novel approach to the EfI-DPR method proposed here uses a combinatorial search algorithm that increases the chances of obtaining the globally optimal solution. Several months worth of data collected by the monitoring system were analyzed using available industry guidelines, including ISO2631-1:1997(E), ISO10137:2007(E) and SCI Publication P354. This enabled the evaluation of the floor performance under real operating conditions.

Abstract: Because of the critical role that bridges play in land transport networks and broader economy, the assessment of existing bridges is gradually becoming a global concern. Structural health monitoring (SHM) systems have been installed on many bridges to provide data for the evaluation of bridge performance and safety. The challenge for bridge engineers is now to make use of the data and convert them into usable information and knowledge. Integrating SHM data with reliability analysis procedures offers a useful and practical methodology for bridge assessment since reliability is an important performance index and reliability-based procedures have the capability of accommodating uncertainties in structural models, responses, loads and monitoring data. In this paper, an approach for integrating SHM data in a reliability assessment framework is proposed. The reliability of the bridge is quantified by incorporating SHM information in the resistance, load and structural models. Advanced modeling tools and techniques, such as finite element analysis, finite model updating and Bayesian updating, are used for the reliability computations. Data from the SHM system installed on the Newmarket Viaduct, a newly constructed, 12-span, post-tensioned box girder bridge erected by the balanced cantilever method in Auckland, New Zealand, are also presented in this paper and used to explain the proposed framework.

Abstract: Time series based Structural Health Monitoring (SHM) methods are being increasingly explored. In this study, Autoregressive (AR) models were used to fit the acceleration time histories of a 3-storey laboratory structure under excitation by earthquake records in several damaged and undamaged states. The coefficients of the AR models were used as inputs into an Artificial Neural Network (ANN) and the ANN was trained to relate the AR coefficients to the damage at each storey. The results showed that proposed method was able to detect, locate and quantify the damage in the structure with a very high accuracy.