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.

Centrifuge modelling on long-term behaviour of tunnels in transitional ground

Long-term settlement of tunnels has caused concerns about its influence on tunnel safety and serviceability. Aiming to investigate the long-term behaviour of tunnels against the background of Shanghai metro line, two cases of centrifuge modelling were conducted, with efforts to expose the mechanism affecting the consolidation of the ground. Evenly layered ground and transitional ground strata were set for each case separately and the settlement, lining load and pore water pressure were checked against elapsed time up to 20 years. The results verified some previous findings concerning the settlement and lining load development trend, however, it was also shown that the transitional ground made the tunnel response more complicated. The research is expected to provide some basis for further research on other affecting factors, such as lining permeability. © 2010 Taylor & Francis Group, London.

Structure learning in a sensorimotor association task.

Learning is often understood as an organism's gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been shown both in cognitive and motor neuroscience that humans are not only able to learn particular stimulus-response mappings, but are also able to extract abstract structural invariants that facilitate generalization to novel tasks. Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects. Using regression and reinforcement learning models we show that the observed facilitation cannot be explained by these basic models of learning stimulus-response associations. We show, however, that the observed data can be explained by a hierarchical Bayesian model that performs structure learning. In line with previous results from cognitive tasks, this suggests that hierarchical Bayesian inference might provide a common framework to explain both the learning of specific stimulus-response associations and the learning of abstract structures that are shared by different task environments.

Learning shape priors for single view reconstruction

In this paper, we aim to reconstruct free-from 3D models from a single view by learning the prior knowledge of a specific class of objects. Instead of heuristically proposing specific regularities and defining parametric models as previous research, our shape prior is learned directly from existing 3D models under a framework based on the Gaussian Process Latent Variable Model (GPLVM). The major contributions of the paper include: 1) a probabilistic framework for prior-based reconstruction we propose, which requires no heuristic of the object, and can be easily generalized to handle various categories of 3D objects, and 2) an attempt at automatic reconstruction of more complex 3D shapes, like human bodies, from 2D silhouettes only. Qualitative and quantitative experimental results on both synthetic and real data demonstrate the efficacy of our new approach. ©2009 IEEE.

Ecodesign innovation: Is 'early' always 'best'?

This paper explores ecodesign within the product development process (PDP), particularly focusing on the design stages. Previous research has highlighted the early stages as the 'best' place to integrate environmental issues. Here the early stage hypothesis is explored from the perspective of the industrial design department - the early stage designers. Being located at the earliest possible design stages of product development would mean that, were the hypothesis to hold true, industrial design would be the 'best' place to locate ecodesign. Empirical research was conducted with the Industrial Design Centre (IDC) of a global Electrical and Electronic goods manufacture. It used a qualitative, inductive research methodology, based on two 'live' design concept projects, participant observation within the department, and on several semi-structured interviews. Throughout this paper, the empirical work is compared and contrasted to ecodesign literature, specifically to models of ecodesign innovation and the product development process. Beginning by exploring of the early stage hypothesis, the paper concludes with a conceptual model of early stage ecodesign for the context in question.

Improving data quality in DSM modelling: A structural comparison approach

The Dependency Structure Matrix (DSM) has proved to be a useful tool for system structure elicitation and analysis. However, as with any modelling approach, the insights gained from analysis are limited by the quality and correctness of input information. This paper explores how the quality of data in a DSM can be enhanced by elicitation methods which include comparison of information acquired from different perspectives and levels of abstraction. The approach is based on comparison of dependencies according to their structural importance. It is illustrated through two case studies: creation of a DSM showing the spatial connections between elements in a product, and a DSM capturing information flows in an organisation. We conclude that considering structural criteria can lead to improved data quality in DSM models, although further research is required to fully explore the benefits and limitations of our proposed approach.