Using an ‘interpretative model’ for contextual design of heritage landscape databases: The case of St Helena Island National Park in Queensland, Australia

Due to the increasing speed of landscape changes and the massive development of computer technologies, the methods of representing heritage landscapes using digital tools have become a worldwide concern in conservation research. The aim of this paper is to demonstrate how an ‘interpretative model’ can be used for contextual design of heritage landscape information systems. This approach is explored through building a geographic information system database for St Helena Island national park in Moreton Bay, South East Queensland, Australia. Stakeholders' interpretations of this landscape were collected through interviews, and then used as a framework for designing the database. The designed database is a digital inventory providing contextual descriptions of the historic infrastructure remnants on St Helena Island. It also reveals the priorities of different sites in terms of historic research, landscape restoration, and tourism development. Additionally, this database produces thematic maps of the intangible heritage values, which could be used for landscape interpretation. This approach is different from the existing methods because building a heritage information system is deemed as an interpretative activity, rather than a value-free replication of the physical environment. This approach also shows how a cultural landscape methodology can be used to create a flexible information system for heritage conservation. The conclusion is that an ‘interpretative model’ of database design facilitates a more explicit focus on information support, and is a potentially effective approach to user-centred design of geographic information systems.

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.

Evolutionary algorithms for sustainable building design

This approach to sustainable design explores the possibility of creating an architectural design process which can iteratively produce optimised and sustainable design solutions. Driven by an evolution process based on genetic algorithms, the system allows the designer to “design the building design generator” rather than to “designs the building”. The design concept is abstracted into a digital design schema, which allows transfer of the human creative vision into the rational language of a computer. The schema is then elaborated into the use of genetic algorithms to evolve innovative, performative and sustainable design solutions. The prioritisation of the project’s constraints and the subsequent design solutions synthesised during design generation are expected to resolve most of the major conflicts in the evaluation and optimisation phases. Mosques are used as the example building typology to ground the research activity. The spatial organisations of various mosque typologies are graphically represented by adjacency constraints between spaces. Each configuration is represented by a planar graph which is then translated into a non-orthogonal dual graph and fed into the genetic algorithm system with fixed constraints and expected performance criteria set to govern evolution. The resultant Hierarchical Evolutionary Algorithmic Design System is developed by linking the evaluation process with environmental assessment tools to rank the candidate designs. The proposed system generates the concept, the seed, and the schema, and has environmental performance as one of the main criteria in driving optimisation.

Design of a Web interface for anatomical images

Interactive documents for use with the World Wide Web have been developed for viewing multi-dimensional radiographic and visual images of human anatomy, derived from the Visible Human Project. Emphasis has been placed on user-controlled features and selections. The purpose was to develop an interface which was independent of host operating system and browser software which would allow viewing of information by multiple users. The interfaces were implemented using HyperText Markup Language (HTML) forms, C programming language and Perl scripting language. Images were pre-processed using ANALYZE and stored on a Web server in CompuServe GIF format. Viewing options were included in the document design, such as interactive thresholding and two-dimensional slice direction. The interface is an example of what may be achieved using the World Wide Web. Key applications envisaged for such software include education, research and accessing of information through internal databases and simultaneous sharing of images by remote computers by health personnel for diagnostic purposes.

How does the size and shape of local populations in China compare to general anthropometric surveys currently used for product design?

Anthropometry has long been used for a range of ergonomic applications & product design. Although products are often designed for specific cohorts, anthropometric data are typically sourced from large scale surveys representative of the general population. Additionally, few data are available for emerging markets like China and India. This study measured 80 Chinese males that were representative of a specific cohort targeted for the design of a new product. Thirteen anthropometric measurements were recorded and compared to two large databases that represented a general population, a Chinese database and a Western database. Substantial differences were identified between the Chinese males measured in this study and both databases. The subjects were substantially taller, heavier and broader than subjects in the older Chinese database. However, they were still substantially smaller, lighter and thinner than Western males. Data from current Western anthropometric surveys are unlikely to accurately represent the target population for product designers and manufacturers in emerging markets like China.

Wood density : a tool to find complementary species for the design of mixed species plantations

The successful establishment and growth of mixed-species forest plantations requires that complementary or facilitatory species be identified. This can be difficult in many tropical areas because the growth characteristics of endemic species are often unknown, particularly when grown at potentially higher densities in plantations than in natural forests. Here, we investigate whether wood density is a useful and readily accessible trait for choosing complementary species for mixed species plantations. Wood density represents the carbon investment per unit volume of stem with a trade-off generally found between fast (low wood density) and slow (high wood density) growing species. To do this, we use data collected from 18 highly diverse mixed species plantations (4–23 mostly native species) aged from 6 to 11 years at the time of data collection located on Leyte Island, Philippines. We found significant negative correlations between wood densities and the height of the most abundant species, as well as with measures of overall stand growth and tree diameter size distribution. Not only do species with denser woods have slower growth rates, but also mixed-species plantations with higher average wood density and higher stem density were also less productive, at least in these young plantations. Similarly, stands with a high diversity in wood densities were less productive. There is growing interest in making greater use of native multi-species mixtures in smallholder and community planting programs in the tropics, and our results show databases of wood density values may help improve their design. In the early development stages of plantations, canopy closure and rapid height growth are usually key silvicultural targets, and wood density values can predict the rapid height development of species. If plantations are being grown for the livelihood of small landholders then the best target is to choose some species with different wood densities. This allows an early harvest of low-wood density species for early income, and will also reduce competition for slower growing trees with higher wood densities for later income generation.

Design, construction and evaluation of systems to predict risk in obstetrics

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy. We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.