The paper proposes a simple method for quick post-earthquake assessment of damage and condition of a stock of bridges in a transportation network using seismic data recorded by a strong motion array. The first part of the paper is concerned with using existing free field strong motion recorders to predict peak ground acceleration (PGA) at an arbitrary bridge site. Two methods are developed using artificial neural networks (a single network and a committee of neural networks) considering influential parameters, such as seismic magnitude, hypocentral depth and epicentral distance. The efficiency of the proposed method is explored using actual strong motion records from the devastating 2010 Darfield and 2011 Christchurch earthquakes in New Zealand. In the second part, two simple ideas are outlined how to infer the likely damage to a bridge using either the predicted PGA and seismic design spectrum, or a broader set of seismic metrics, structural parameters and damage indices.
The Canterbury earthquake series of 2010/2011 has turned the city of Christchurch into a full scale natural laboratory testing the structural and non-structural response of buildings under moderate to very severe earthquake shaking. The lessons learned from this, which have come at great cost socially and economically, are extremely valuable in increasing our understanding of whole building performance in severe earthquakes. Given current initiatives underway on both sides of the Tasman towards developing joint Australasian steel and composite steel/concrete design and construction standards that would span a very wide range of geological conditions and seismic zones, these lessons are relevant to both countries. This paper focusses on the performance of steel framed buildings in Christchurch city, with greatest emphasis on multi-storey buildings, but also covering single storey steel framed buildings and light steel framed housing. It addresses such issues as the magnitude and structural impact of the earthquake series, importance of good detailing, lack of observed column base hinging, the excellent performance of composite floors and it will briefly cover research underway to quantify some of these effects for use in design.
Having a quick but reliable insight into the likelihood of damage to bridges immediately after an earthquake is an important concern especially in the earthquake prone countries such as New Zealand for ensuring emergency transportation network operations. A set of primary indicators necessary to perform damage likelihood assessment are ground motion parameters such as peak ground acceleration (PGA) at each bridge site. Organizations, such as GNS in New Zealand, record these parameters using distributed arrays of sensors. The challenge is that those sensors are not installed at, or close to, bridge sites and so bridge site specific data are not readily available. This study proposes a method to predict ground motion parameters for each bridge site based on remote seismic array recordings. Because of the existing abundant source of data related to two recent strong earthquakes that occurred in 2010 and 2011 and their aftershocks, the city of Christchurch is considered to develop and examine the method. Artificial neural networks have been considered for this research. Accelerations recorded by the GeoNet seismic array were considered to develop a functional relationship enabling the prediction of PGAs. http://www.nzsee.org.nz/db/2013/Posters.htm
