Earthquake Risk and Engineering towards a Resilient World

9 - 10 July 2015, Homerton College, Cambridge, UK


SECED 2015 was a two-day conference on Earthquake and Civil Engineering Dynamics that took place on 9-10th July 2015 at Homerton College, Cambridge.

This was the first major conference to be held in the UK on this topic since SECED hosted the 2002 European Conference on Earthquake Engineering in London.

The conference brought together experts from a broad range of disciplines, including structural engineering, nuclear engineering, seismology, geology, geotechnical engineering, urban development, social sciences, business and insurance; all focused on risk, mitigation and recovery.

Conference themes

  • Geotechnical earthquake engineering
  • Seismic design for nuclear facilities
  • Seismic hazard and engineering seismology
  • Masonry structures
  • Risk and catastrophe modelling
  • Vibrations, blast and civil engineering dynamics
  • Dams and hydropower
  • Seismic assessment and retrofit of engineered and non-engineered structures
  • Social impacts and community recovery

Keynote speakers

SECED 2015 featured the following keynote speakers (affiliations correct at the time of the conference):

  • Peter Ford and Tim Allmark, Office for Nuclear Regulation, UK
  • Don Anderson, CH2M HILL, Seattle, USA
  • Bernard Dost, Royal Netherlands Meteorological Institute, The Netherlands
  • Anne Kiremidjian, Stanford University, USA
  • Rob May, Golder Associates, Australia
  • Tiziana Rossetto, University College London, UK
  • Andrew Whittaker, University at Buffalo, USA
  • Mike Willford, Arup, The Netherlands

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Incremental Dynamic Analysis (IDA) is a powerful method for the seismic performance assessment of structures. IDA is also very efficient for handling epistemic uncertainty, i.e. uncertainty due to the mechanical properties of the structure. In the latter case, IDA should be performed within a Monte Carlo framework requiring the execution of a vast number of nonlinear response history analyses. The increased computing effort renders the calculation of performance statistics time-consuming and hence the method is not always practical. We propose a scheme based on artificial neural networks (NN) in order to reduce the computational effort and allow using the method on many applications. Within a Monte Carlo framework, trained Neural Networks can rapidly generate a large sample of IDA curves which are then post-processed to calculate useful response statistics. The implementation of the proposed method is quick, straightforward and gives accurate seismic performance estimations.

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