25 research outputs found
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Promoting good academic practice through the curriculum and project work
This paper outlines a University wide initiative introduced at City University London, the underlying aim of which is to develop good academic practice skills amongst students and discourage them from undertaking plagiarism or other forms of academic misconduct. The initiative is organised under three projects, which are being undertaken by a set of eight Educational Development Associates (EDAs) - existing academics acting as ‘change agents’ within their Schools. The paper focuses on the first project that EDAs undertook, that being the Learning Activity Project. This involved EDAs working with staff within their Schools to develop new, formative, programme-specific learning activities, to be undertaken by all students in the first term of their studies, and with the aim of providing students with practice in the study skills that they need to demonstrate in subsequent assessments of that programme. After reviewing relevant literature on academic conduct issues, the main body of the paper provides three case studies, each of which details the development of a learning activity in one of the Schools of the University. These learning activities share a common element, in that they are all delivered using a piece of software called OLIVIA. However, for purposes of this paper, each case study details specific aspects of the relevant learning activity, such that readers are provided with a broad perspective of the experiences of implementation of the project through the lenses of different Schools. The last section of the paper details the evaluative mechanism that is being used for the initiative as a whole
Masonry compressive strength prediction using artificial neural networks
The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.- (undefined
Probabilistic seismic assessment of reinforced concrete buildings with and without masonry infills
SIGLEAvailable from British Library Document Supply Centre-DSC:DXN035945 / BLDSC - British Library Document Supply CentreGBUnited Kingdo