A collaborative digestion and design game for community and technology exploration

In this paper, we present interim results from the Communities and Place project, which is exploring methods for understanding communities in a variety of contexts, and how to inform the design of technology to support them. We report on our experience with adapting an existing game-based approach for working with video as a resource in participatory design processes. Our adaptations allow the approach to be used with diverse data arising out of the different communities we are engaged with, and different design traditions we approach the problem from, leading to the formation of common design themes to inform our future work on this project.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

What are the lessons for entrepreneurship from creativity and design?

Entrepreneurship and innovation make significant contributions to the success of firms and both notions are often linked directly or indirectly to notions of creativity and more recently design (Drucker, 1985; Kelley, 2001; Nystrom, 1993). This theoretical paper investigates the processes involved in entrepreneurship, creativity and design and identifies key processes relevant to entrepreneurial practice.

Behaviour and design of cold-formed steel compression members at elevated temperatures

Cold-formed steel members have been widely used in residential, industrial and commercial buildings as primary load bearing structural elements and non-load bearing structural elements (partitions) due to their advantages such as higher strength to weight ratio over the other structural materials such as hot-rolled steel, timber and concrete. Cold-formed steel members are often made from thin steel sheets and hence they are more susceptible to various buckling modes. Generally short columns are susceptible to local or distortional buckling while long columns to flexural or flexural-torsional buckling. Fire safety design of building structures is an essential requirement as fire events can cause loss of property and lives. Therefore it is essential to understand the fire performance of light gauge cold-formed steel structures under fire conditions. The buckling behaviour of cold-formed steel compression members under fire conditions is not well investigated yet and hence there is a lack of knowledge on the fire performance of cold-formed steel compression members. Current cold-formed steel design standards do not provide adequate design guidelines for the fire design of cold-formed steel compression members. Therefore a research project based on extensive experimental and numerical studies was undertaken at the Queensland University of Technology to investigate the buckling behaviour of light gauge cold-formed steel compression members under simulated fire conditions. As the first phase of this research, a detailed review was undertaken on the mechanical properties of light gauge cold-formed steels at elevated temperatures and the most reliable predictive models for mechanical properties and stress-strain models based on detailed experimental investigations were identified. Their accuracy was verified experimentally by carrying out a series of tensile coupon tests at ambient and elevated temperatures. As the second phase of this research, local buckling behaviour was investigated based on the experimental and numerical investigations at ambient and elevated temperatures. First a series of 91 local buckling tests was carried out at ambient and elevated temperatures on lipped and unlipped channels made of G250-0.95, G550-0.95, G250-1.95 and G450-1.90 cold-formed steels. Suitable finite element models were then developed to simulate the experimental conditions. These models were converted to ideal finite element models to undertake detailed parametric study. Finally all the ultimate load capacity results for local buckling were compared with the available design methods based on AS/NZS 4600, BS 5950 Part 5, Eurocode 3 Part 1.2 and the direct strength method (DSM), and suitable recommendations were made for the fire design of cold-formed steel compression members subject to local buckling. As the third phase of this research, flexural-torsional buckling behaviour was investigated experimentally and numerically. Two series of 39 flexural-torsional buckling tests were undertaken at ambient and elevated temperatures. The first series consisted 2800 mm long columns of G550-0.95, G250-1.95 and G450-1.90 cold-formed steel lipped channel columns while the second series contained 1800 mm long lipped channel columns of the same steel thickness and strength grades. All the experimental tests were simulated using a suitable finite element model, and the same model was used in a detailed parametric study following validation. Based on the comparison of results from the experimental and parametric studies with the available design methods, suitable design recommendations were made. This thesis presents a detailed description of the experimental and numerical studies undertaken on the mechanical properties and the local and flexural-torsional bucking behaviour of cold-formed steel compression member at ambient and elevated temperatures. It also describes the currently available ambient temperature design methods and their accuracy when used for fire design with appropriately reduced mechanical properties at elevated temperatures. Available fire design methods are also included and their accuracy in predicting the ultimate load capacity at elevated temperatures was investigated. This research has shown that the current ambient temperature design methods are capable of predicting the local and flexural-torsional buckling capacities of cold-formed steel compression members at elevated temperatures with the use of reduced mechanical properties. However, the elevated temperature design method in Eurocode 3 Part 1.2 is overly conservative and hence unsuitable, particularly in the case of flexural-torsional buckling at elevated temperatures.