The Second Life of urban planning? Using neogeography tools for community engagement

The majority of the world’s citizens now live in cities. Although urban planning can thus be thought of as a field with significant ramifications on the human condition, many practitioners feel that it has reached the crossroads in thought leadership between traditional practice and a new, more participatory and open approach. Conventional ways to engage people in participatory planning exercises are limited in reach and scope. At the same time, socio-cultural trends and technology innovation offer opportunities to re-think the status quo in urban planning. Neogeography introduces tools and services that allow non-geographers to use advanced geographical information systems. Similarly, is there potential for the emergence of a neo-planning paradigm in which urban planning is carried out through active civic engagement aided by Web 2.0 and new media technologies thus redefining the role of practicing planners? This paper traces a number of evolving links between urban planning, neogeography and information and communication technology. Two significant trends – participation and visualisation – with direct implications for urban planning are discussed. Combining advanced participation and visualisation features, the popular virtual reality environment Second Life is then introduced as a test bed to explore a planning workshop and an integrated software event framework to assist narrative generation. We discuss an approach to harness and analyse narratives using virtual reality logging to make transparent how users understand and interpret proposed urban designs.

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.

STRATÉGÉ – Study of Transparent Recognition, Accreditation Tools and Experience in a Global Environment

Engineering graduates of today, face a working environment that assumes global mobility in the labour market. This challenge means, amongst universities worldwide, a demand to increase the globalisation of educational programs, context, and increase and support the mobility of students through mechanisms such as student exchange and double masters degrees. Engineering student mobility from Australia is low with only a few Engineering Faculties encouraging students to go internationally. This comparative study, using universities in Australia and Europe, of feedback from students who have been on exchange or proposing to go on exchange, employers and faculty addresses the motivators and barriers to student mobility and exchange from the perspectives of the university, faculty, students and employers. Recommendations will be presented on how student mobility and exchange can be improved, and mechanisms such as double Masters Degrees, dual accreditation and Erasmus Mundus 2009 – 2013 can be utilised to improve student mobility.

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.

International review of environmental assessment tools and databases

Existing widely known environmental assessment models, primarily those for Life Cycle Assessment of manufactured products and buildings, were reviewed to grasp their characteristics, since the past several years have seen a significant increase in interest and research activity in the development of building environmental assessment methods. Each method or tool was assessed under the headings of description, data requirement, end-use, assessment criteria (scale of assessment and scoring/ weighting system)and present status

Nation & internation practices in decision support tools in road asset management

This document provides a review of international and national practices in investment decision support tools in road asset management. Efforts were concentrated on identifying analytic frameworks, evaluation methodologies and criteria adopted by current tools. Emphasis was also given to how current approaches support Triple Bottom Line decision-making. Benefit Cost Analysis and Multiple Criteria Analysis are principle methodologies in supporting decision-making in Road Asset Management. The complexity of the applications shows significant differences in international practices. There is continuing discussion amongst practitioners and researchers regarding to which one is more appropriate in supporting decision-making. It is suggested that the two approaches should be regarded as complementary instead of competitive means. Multiple Criteria Analysis may be particularly helpful in early stages of project development, say strategic planning. Benefit Cost Analysis is used most widely for project prioritisation and selecting the final project from amongst a set of alternatives. Benefit Cost Analysis approach is useful tool for investment decision-making from an economic perspective. An extension of the approach, which includes social and environmental externalities, is currently used in supporting Triple Bottom Line decision-making in the road sector. However, efforts should be given to several issues in the applications. First of all, there is a need to reach a degree of commonality on considering social and environmental externalities, which may be achieved by aggregating the best practices. At different decision-making level, the detail of consideration of the externalities should be different. It is intended to develop a generic framework to coordinate the range of existing practices. The standard framework will also be helpful in reducing double counting, which appears in some current practices. Cautions should also be given to the methods of determining the value of social and environmental externalities. A number of methods, such as market price, resource costs and Willingness to Pay, are found in the review. The use of unreasonable monetisation methods in some cases has discredited Benefit Cost Analysis in the eyes of decision makers and the public. Some social externalities, such as employment and regional economic impacts, are generally omitted in current practices. This is due to the lack of information and credible models. It may be appropriate to consider these externalities in qualitative forms in a Multiple Criteria Analysis. Consensus has been reached in considering noise and air pollution in international practices. However, Australia practices generally omitted these externalities. Equity is an important consideration in Road Asset Management. The considerations are either between regions, or social groups, such as income, age, gender, disable, etc. In current practice, there is not a well developed quantitative measure for equity issues. More research is needed to target this issue. Although Multiple Criteria Analysis has been used for decades, there is not a generally accepted framework in the choice of modelling methods and various externalities. The result is that different analysts are unlikely to reach consistent conclusions about a policy measure. In current practices, some favour using methods which are able to prioritise alternatives, such as Goal Programming, Goal Achievement Matrix, Analytic Hierarchy Process. The others just present various impacts to decision-makers to characterise the projects. Weighting and scoring system are critical in most Multiple Criteria Analysis. However, the processes of assessing weights and scores were criticised as highly arbitrary and subjective. It is essential that the process should be as transparent as possible. Obtaining weights and scores by consulting local communities is a common practice, but is likely to result in bias towards local interests. Interactive approach has the advantage in helping decision-makers elaborating their preferences. However, computation burden may result in lose of interests of decision-makers during the solution process of a large-scale problem, say a large state road network. Current practices tend to use cardinal or ordinal scales in measure in non-monetised externalities. Distorted valuations can occur where variables measured in physical units, are converted to scales. For example, decibels of noise converts to a scale of -4 to +4 with a linear transformation, the difference between 3 and 4 represents a far greater increase in discomfort to people than the increase from 0 to 1. It is suggested to assign different weights to individual score. Due to overlapped goals, the problem of double counting also appears in some of Multiple Criteria Analysis. The situation can be improved by carefully selecting and defining investment goals and criteria. Other issues, such as the treatment of time effect, incorporating risk and uncertainty, have been given scant attention in current practices. This report suggested establishing a common analytic framework to deal with these issues.

Decision support tools for concrete infrastructure rehabilitation (using FRP composites)

This report presents a summary of the research conducted by the research team of the CRC project 2002-005-C, “Decision support tools for concrete infrastructure rehabilitation”. The project scope, objectives, significance and innovation and the research methodology is outlined in the introduction, which is followed by five chapters covering different aspects of the research completed. Major findings of a review of literature conducted covering both use of fibre reinforced polymer composites in rehabilitation of concrete bridge structures and decision support frameworks in civil infrastructure asset management is presented in chapter two. Case study of development of a strengthening scheme for the “Tenthill Creek bridge” is covered in the third chapter, which summarises the capacity assessment, traditional strengthening solution and the innovative solution using FRP composites. The fourth chapter presents the methodology for development of a user guide covering selection of materials, design and application of FRP in strengthening of concrete structures, which were demonstrated using design examples. Fifth chapter presents the methodology developed for evaluating whole of life cycle costing of treatment options for concrete bridge structures. The decision support software tool developed to compare different treatment options based on reliability based whole of life cycle costing will be briefly described in this chapter as well. The report concludes with a summary of findings and recommendations for future research.

ARCS collaboration tools

The Australian Research Collaboration Service (ARCS) has been supporting a wide range of Collaboration Services and Tools which have been allowing researchers, groups and research communities to share ideas and collaborate across organisational boundaries.----- This talk will give an introduction to a number of exciting technologies which are now available. Focus will be on two main areas of Video Collaboration Tools, allowing researchers to talk face-to-face and share data in real-time, and Web Collaboration Tools, allowing researchers to share information and ideas with other like-minded researchers irrespective of distance or organisational structure. A number of examples will also be shown of how these technologies have been used with in various research communities.----- A brief introduction will be given to a number of services which ARCS is now operating and/or supporting such as:--- * EVO – A video conferencing application, which is particularly suited to desktop or low bandwidth applications.--- * AccessGrid – An open source video conferencing and collaboration tool kit, which is great for room to room meetings.--- * Sakai – An online collaboration and learning environment, support teaching and learning, ad hoc group collaboration, support for portfolios and research collaboration.--- * Plone – A ready-to-run content management system, that provides you with a system for managing web content that is ideal for project groups, communities, web sites, extranets and intranets.--- * Wikis – A way to easily create, edit, and link pages together, to create collaborative websites.

ARCS collaboration tools and support workshop

Registration fees for this workshop are being met by ARCS. There is no cost to attend; however space is limited.----- The Australian Research Collaboration Service (ARCS) has been supporting a wide range of Collaboration Services and Tools which have been allowing researchers, groups and research communities to share ideas and collaborate across organisational boundaries.----- This workshop will give an introduction into a number of web based and real-time collaboration tools and services which researchers may find useful for day-to-day collaboration with members of a research team located within an institution or across institutions. Attendees will be shown how a number of these tools work with strong emphasis placed on how these tools can help facilitate communication and collaboration. Attendees will have the opportunity to try out a number of examples themselves, and interact with the workshop staff to discuss how their own use cases could benefit from the tools and services which can be provided.----- Outline: A hands on introduction will be given to a number of services which ARCS is now operating and/or supporting such as:--- * EVO – A video conferencing environment, which is particularly suited to desktop or low bandwidth applications.--- * AccessGrid – An open source video conferencing and collaboration tool kit, which is great for room to room meetings.--- * Sakai – An online collaboration and learning environment, support teaching and learning, ad hoc group collaboration, support for portfolios and research collaboration.--- * Plone and Drupal – A ready-to-run content management system, that provides you with a system for managing web content that is ideal for project groups, communities, web sites, extranets and intranets.--- * Wikis – A way to easily create, edit, and link pages together, to create collaborative websites.

Re-life of buildings - decision support tools for maximising project efficiency

This paper describes the process adopted in developing an integrated decision support framework for planning of office building refurbishment projects, with specific emphasize on optimising rentable floor space, structural strengthening, residual life and sustainability. Expert opinion on the issues to be considered in a tool is being captured through the DELPHI process, which is currently ongoing. The methodology for development of the integrated tool will be validated through decisions taken during a case study project: refurbishment of CH1 building of Melbourne City Council, which will be followed through to completion by the research team. Current status of the CH1 planning will be presented in the context of the research project.

Learning Strategies for Digital Tools

Learning a digital tool is often a hidden process. We tend to learn new tools in a bewildering range of ways. Formal, informal, structured, random, conscious, unconscious, individual, group strategies, may all play a part, but are often lost to us in the complex and demanding processes of learning. But when we reflect carefully on the experience, some patterns and surprising techniques emerge. This monograph presents the thinking of seven students in MDN642, Digital Pedagogies, where they have deliberately reflected on the mental processes at work as they learnt a digital technology of their choice.

Towards continuous surveillance of fruit flies using sensor networks and machine vision

In Australia, the Queensland fruit fly (B. tryoni), is the most destructive insect pest of horticulture, attacking nearly all fruit and vegetable crops. This project has researched and prototyped a system for monitoring fruit flies so that authorities can be alerted when a fly enters a crop in a more efficient manner than is currently used. This paper presents the idea of our sensor platform design as well as the fruit fly detection and recognition algorithm by using machine vision techniques. Our experiments showed that the designed trap and sensor platform is capable to capture quality fly images, the invasive flies can be successfully detected and the average precision of the Queensland fruit fly recognition is 80% from our experiment.

Assessing the energy efficiency of gear oils via the FZG test machine

Energy efficient lubricants are becoming increasingly popular. This is due to a global increase in environmental awareness combined with the potential of reducing operating costs. A new test method of evaluating the energy efficiency of gear oils has been described in this report. The method involves measuring the power required by an FZG test rig to run while using a particular test lubricant. For each oil that was being evaluated, the rig was run for 10 minutes at a load stage of 10. Six extreme pressure (EP) industrial gear oils of mineral base were tested. The difference in power requirements between the best and the worst performing oils was 2.77 and 3.24 kW, respectively. This equates to a 14.6% reduction in power, a significant amount if considered in relation to a high powered industrial machine. The oils of superior performance were noticed to run at reduced temperatures. They were also more expensive than the other products of lesser performance.

Integrated Diagnosis and Prognosis Model for High Pressure LNG Pump

In condition-based maintenance (CBM), effective diagnostics and prognostics are essential tools for maintenance engineers to identify imminent fault and to predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedules production if necessary. This paper presents a technique for accurate assessment of the remnant life of machines based on historical failure knowledge embedded in the closed loop diagnostic and prognostic system. The technique uses the Support Vector Machine (SVM) classifier for both fault diagnosis and evaluation of health stages of machine degradation. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for multi-class fault diagnosis. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

Scheduling trains as a blocking parallel-machine job shop scheduling problem

In this paper, the train scheduling problem is modelled as a blocking parallel-machine job shop scheduling (BPMJSS) problem. In the model, trains, single-track sections and multiple-track sections, respectively, are synonymous with jobs, single machines and parallel machines, and an operation is regarded as the movement/traversal of a train across a section. Due to the lack of buffer space, the real-life case should consider blocking or hold-while-wait constraints, which means that a track section cannot release and must hold the train until next section on the routing becomes available. Based on literature review and our analysis, it is very hard to find a feasible complete schedule directly for BPMJSS problems. Firstly, a parallel-machine job-shop-scheduling (PMJSS) problem is solved by an improved shifting bottleneck procedure (SBP) algorithm without considering blocking conditions. Inspired by the proposed SBP algorithm, feasibility satisfaction procedure (FSP) algorithm is developed to solve and analyse the BPMJSS problem, by an alternative graph model that is an extension of the classical disjunctive graph models. The proposed algorithms have been implemented and validated using real-world data from Queensland Rail. Sensitivity analysis has been applied by considering train length, upgrading track sections, increasing train speed and changing bottleneck sections. The outcomes show that the proposed methodology would be a very useful tool for the real-life train scheduling problems