Quantitative comparison between the use of 3D vs 2D visualization tools to present building design proposals to non-spatial skilled end users

Information technology in construction (ITC) has been gaining wide acceptance and is being implemented in the construction research domains as a tool to assist decision makers. Most of the research into visualization technologies (VT) has been on the wide range of 3D and simulation applications suitable for construction processes. Despite its development with interoperability and standardization of products, VT usage has remained very low when it comes to communicating and addressing the needs of building end-users (BEU). This paper argues that building end users are a source of experience and expertise that can be brought into the briefing stage for the evaluation of design proposals. It also suggests that the end user is a source of new ideas promoting innovation. In this research a positivistic methodology that includes the comparison of 3D models and the traditional 2D methods is proposed. It will help to identify "how much", if anything, a non-spatial specialist can gain in terms Of "understanding" of a particular design proposal presented, using both methods.

A review of the use of demonstration projects to achieve widespread adoption of innovative practices

The UK construction industry has embarked on one of the largest experiments in the implementation of innovative technologies and practices in its history. Following Rethinking Construction[1], generally known as the Egan Report, the Movement for Innovation was established with the aim of using demonstration projects as practical examples of innovation in order to encourage others to follow the example. The number of demonstration projects has exceeded the original plans and more are being added. This paper reviews the approach in terms of the practice of using demonstration projects to achieve widespread take up of innovation, the modifications to the programme and its management and considers future developments to improve its, effectiveness.

Evaluating the use of multimedia authoring with dyslexic learners: a case study

This paper reports on the findings of a case study set up to explore the possible benefits that dyslexic learners might have when engaging in the creation of their own multimedia project. Two children with specific learning difficulties worked with the author to develop a multimedia presentation. The children developed authoring skills (such as planning and drafting, composition, revision and reflection, proof reading and presentation) and became active, motivated learners. It is believed that the open-ended character of a multimedia authoring package can encourage creative thinking and interest for content and style of presentation.

The use of drama to teach social skills in a special school setting for students with autism.

For children with autism, social challenges may be both part of the disability and a barrier to accessing education. This paper reports on a project which used drama to address such challenges by drawing on the social skills of non-autistic peers in a special school setting. The paper demonstrates how drama’s flexibility may be harnessed in order to help students support each other’s development of creative and communicative skills. Focusing on two children in particular, specific examples are given to illustrate how they participated in group work, made imaginative contributions to verbal and physical representations, and engaged with abstract ideas. The project’s outcomes suggest, given a concrete structure and an invitation to collaborate, drama can be a powerful learning medium for children with ASD. The conclusion reflects on the diverse meaning of inclusive practice which can be achieved within specialist settings.

An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.