Bridge fragility curves, which express the probability of a bridge reaching a certain damage state for a given ground motion parameter, play an important role in the overall seismic risk assessment of a transportation network. Current analytical methodologies for generating bridge fragility curves do not adequately account for all major contributing bridge components. Studies have shown that for some bridge types, neglecting to account for all of these components can lead to a misrepresentation of the bridges' overall fragilities.

In this study, an expanded methodology for the generation of analytical fragility curves for highway bridges is presented. This methodology considers the contribution of the major components of the bridge, such as the columns, bearings and abutments, to its overall bridge system fragility. In particular, this methodology utilizes probability tools to directly estimate the bridge system fragility from the individual component fragilities. This is illustrated using a bridge whose construction and configuration are typical to the Central and Southeastern United States and the results are presented and discussed herein. This study shows that the bridge as a system is more fragile than any one of the individual components. Assuming that the columns represent the entire bridge system can result in errors as large as 50% at higher damage states. This provides support to the assertion that multiple bridge components should be considered in the development of bridge fragility curves. The findings also show that estimation of the bridge fragilities by their first-order bounds could result in errors of up to 40%. Copyright © 2006 John Wiley & Sons, Ltd.

Seismic fragility methodology for highway bridges using a component level approach

The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. In this paper, a new hybrid time series neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks (ANNs). The proposed approach consists of an overall modelling framework, which is a combination of the conventional and ANN techniques. The steps involved in the time series analysis, e.g. de-trending and de-seasonalisation, can be carried out before gradually presenting the modified time series data to the ANN. The proposed hybrid approach for time series forecasting is tested using the monthly streamflow data at Colorado River at Lees Ferry, USA. Specifically, results from four time series models of auto-regressive (AR) type and four ANN models are presented. The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts. Although the proposed hybrid neural network models are applied in hydrology in this study, they have tremendous scope for application in a wide range of areas for achieving increased accuracies in time series forecasting.

Hybrid neural network models for hydrologic time series forecasting

iii ACKNOWLEDGMENTS iv TABLE OF CONTENTS vii LIST OF FIGURES xvii LIST OF TABLES xxv

Assessing seismic collapse safety of modern reinforced concrete moment frame buildings

Analytical Fragility Curves for Highway Bridges in Moderate Seismic Zones

The Guangzhou New TV Tower (GNTVT), currently being constructed in Guangzhou, China, is a supertall structure with a height of 610 m. This tube-in-tube structure comprises a reinforced concrete inner tube and a steel outer tube adopting concrete-filled-tube columns. A sophisticated structural health monitoring (SHM) system consisting of over 600 sensors has been designed and is being implemented by The Hong Kong Polytechnic University to GNTVT for both in-construction and in-service real-time monitoring. This paper outlines the technology innovation in developing and implementing this SHM system, which includes (i) modular design of the SHM system, (ii) integration of the in-construction monitoring system and the in-service monitoring system, (iii) wireless-based data acquisition and Internet-based remote data transmission, (iv) design and implementation of a fiber Bragg grating sensing system,(v) structural health and condition assessment using static and dynamic monitoring data, (vi) verification of the effectiveness of vibration control devices by the SHM system, and (vii) development of an SHM benchmark problem by taking GNTVT as a test bed and using real-world measurement data. Preliminary monitoring data including those obtained during the Wenchuan earthquake and recent typhoons are also presented. Copyright © 2008 John Wiley & Sons, Ltd.

Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower

Only when the data base is of sufficient quality can a theoretical model of any process be justified. Such a data base has recently been assembled in the field of damping in tall buildings. It is now possible to specify a damping value appropriate to a particular building vibrating at a specified amplitude which is well correlated with values which are measured in practice. The mechanism on which this predictor is based suggests that the predictor will be applicable to other types of structure as well. As yet the data base for these other structures does not exist, but some circumstantial evidence is encouraging.

A.P. Jeary

Papers

Damping in tall buildings—a mechanism and a predictor

Field measurements of typhoon effects on a super tall building

Damping in structures

The effect of amplitude-dependent damping on wind-induced vibrations of a super tall building

The description and measurement of nonlinear damping in structures