Abstract
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.
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the New Zealand Society for Earthquake Engineering Annual Conference 2013 |
| Pages | 1-8 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 26 Apr 2013 |
| Event | New Zealand Society for Earthquake Engineering Technical Conference and AGM - Wellington, New Zealand Duration: 26 Apr 2013 → 28 Apr 2013 |
Conference
| Conference | New Zealand Society for Earthquake Engineering Technical Conference and AGM |
|---|---|
| Country/Territory | New Zealand |
| City | Wellington |
| Period | 26/04/13 → 28/04/13 |
Bibliographical note
ACKNOWLEDGEMENTSThe authors would like to express their gratitude to their supporters. Research work at the University of Auckland was supported by the Natural Hazards Platform grant UAOM11/15-4.3. Piotr Omenzetter’s work within The LRF Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by The Lloyd's Register Foundation (The LRF). The LRF funds education, training and research programmes in transportation, science, engineering, technology and the safety of life, worldwide for the benefit of all.
