Centrifuge modelling-based verification of response spectra design methods for some vulnerable structures

Residential RC framed structures suffered heavily during the 2001 Bhuj earthquake in Gujarat, India. These types of structures also saw severe damage in other earthquakes such as the 1999 Kocaeli earthquake in Turkey and 921 Ji-Ji earthquake in Taiwan. In this paper the seismic response of residential structures was investigated using physical modelling. Idealised soft storey and top heavy, two degrees of freedom (2DOF) portal frame structures were developed and tested on saturated and dry sand models at 25 g using the Schofield Centre 10-m Beam Centrifuge. It was possible to recreate observed field behaviour using these models. As observed in many of the recent earthquakes, soft storey structures were found to be particularly vulnerable to seismic loads. Elastic response spectra methods are often used in the design of simple portal frame structures. The seismic risk of these structures can be significantly increased due to modifications such as removal of a column or addition of heavy water tanks on the roof. The experimental data from the dynamic centrifuge tests on such soft storey or top-heavy models was used to evaluate the predictions obtained from the response spectra. Response spectra were able to predict seismic response during small to moderate intensity earthquakes, but became inaccurate during strong earthquakes and when soil structure interaction effects became important. Re-evaluation of seismic risk of such modified structures is required and time domain analyses suggested by building codes such as IBC, UBC or NEHRP may be more appropriate. © Springer 2006.

Natural versus artificial scene classification by ordering discrete fourier power spectra

Holistic representations of natural scenes is an effective and powerful source of information for semantic classification and analysis of arbitrary images. Recently, the frequency domain has been successfully exploited to holistically encode the content of natural scenes in order to obtain a robust representation for scene classification. In this paper, we present a new approach to naturalness classification of scenes using frequency domain. The proposed method is based on the ordering of the Discrete Fourier Power Spectra. Features extracted from this ordering are shown sufficient to build a robust holistic representation for Natural vs. Artificial scene classification. Experiments show that the proposed frequency domain method matches the accuracy of other state-of-the-art solutions. © 2008 Springer Berlin Heidelberg.