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.

Business Service Management

Business Service Management describes the emerging discipline dedicated to the IT-enabled management of services as corporate assets. Business Service Management deals with the service orientation of the organisation and the provisioning and use of business services. The term business service describes an autonomous transformational capability that is offered to and consumed by external or internal customers for their benefit. The prefix ‘business’ stresses that such a service has a market value, requires the ability to be managed internally as a corporate asset and that its implementation is technology-agnostic. While business services (or so called capabilities) have attracted the attention of many vendors and organisations, a lack of understanding of the activities required for the successful management of such business services remains a critical issue. In order to fill this gap, a framework consisting of Service Lifecycle Management, Service Value Management, Service Relationship Management and Service Enablement is proposed. This Framework has the potential to provide organisations with the much needed guidance in their attempts to convert current IT-driven service initiatives into successful service-centric business models.

Controlled flexibility and lifecycle management of business processes through extensibility

Vendors provide reference process models as consolidated, off-the-shelf solutions to capture best practices in a given industry domain. Customers can then adapt these models to suit their specific requirements. Traditional process flexibility approaches facilitate this operation, but do not fully address it as they do not sufficiently take controlled change guided by vendors' reference models into account. This tension between the customer's freedom of adapting reference models, and the ability to incorporate with relatively low effort vendor-initiated reference model changes, thus needs to be carefully balanced. This paper introduces process extensibility as a new paradigm for customizing reference processes and managing their evolution over time. Process extensibility mandates a clear recognition of the different responsibilities and interests of reference model vendors and consumers, and is concerned with keeping the effort of customer-side reference model adaptations low while allowing sufficient room for model change.

Improving the management of building life cycle : a data mining approach

The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

Business and software service lifecycle management

Although the service-oriented paradigm has been well established in the technical domain for quite some time now, service governance is still considered a research gap. To ensure adequate governance, there is a necessity to manage services as first-class assets throughout the lifecycle. Now that the concept of ser-vice-orientation is also increasingly applied on the business level to structure an organisation’s capabili-ties, the problem has become an even bigger chal-lenge. This paper presents a generic business and software service lifecycle and aligns it with the com-mon management layers in organisations. Using ser-vice analysis as an example, it moreover illustrates how activities in the service lifecycle may vary on lower levels of granularity depending on the focus on business or software services.

Developing Software Agents for Data Mining : [AIMM] Agents for Improving Maintenance Management

This report demonstrates the development of: • Development of software agents for data mining • Link data mining to building model in virtual environments • Link knowledge development with building model in virtual environments • Demonstration of software agents for data mining • Populate with maintenance data

Demonstration of Software Agents Prototype System : [AIMM] Agents for Improving Maintenance Management

This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. This report presents the demonstration of software agents prototype system for improving maintenance management [AIMM] including: • Developing and implementing a user focused approach for mining the maintenance data of buildings. • Refining the development of a multi agent system for data mining in virtual environments (Active Worlds) by developing and implementing a filtering agent on the results obtained from applying data mining techniques on the maintenance data. • Integrating the filtering agent within the multi agents system in an interactive networked multi-user 3D virtual environment. • Populating maintenance data and discovering new rules of knowledge.

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.

Management, organisation, and ethics in the public sector

Since the 1980s, in Australia and other developed nations, public sector management philosophies and how the public sector is organized have changed dramatically. At the same time, there have been many demands, and several attempts, to preserve and promote ethical behaviour within the public sector - though few go much beyond the publication of a code. Both developments require an understanding of how public organizations operate in this new environment. Organizational and management theory are seen as providing important potential insights into the opportunities and pitfalls for building ethics into the practices, culture and norms of public organizations. This book brings together the experience and research of a range of "reflective practitioners" and "engaged academics" in public sector management, organizational theory, management theory, public sector ethics and law. It addresses what management and organization theory might suggest about the nature of public organizations and the institutionalization of ethics.

Building Information Modelling for FM at Sydney Opera House

“SOH see significant benefit in digitising its drawings and operation and maintenance manuals. Since SOH do not currently have digital models of the Opera House structure or other components, there is an opportunity for this national case study to promote the application of Digital Facility Modelling using standardized Building Information Models (BIM)”. The digital modelling element of this project examined the potential of building information models for Facility Management focusing on the following areas: • The re-usability of building information for FM purposes • BIM as an Integrated information model for facility management • Extendibility of the BIM to cope with business specific requirements • Commercial facility management software using standardised building information models • The ability to add (organisation specific) intelligence to the model • A roadmap for SOH to adopt BIM for FM The project has established that BIM – building information modelling - is an appropriate and potentially beneficial technology for the storage of integrated building, maintenance and management data for SOH. Based on the attributes of a BIM, several advantages can be envisioned: consistency in the data, intelligence in the model, multiple representations, source of information for intelligent programs and intelligent queries. The IFC – open building exchange standard – specification provides comprehensive support for asset and facility management functions, and offers new management, collaboration and procurement relationships based on sharing of intelligent building data. The major advantages of using an open standard are: information can be read and manipulated by any compliant software, reduced user “lock in” to proprietary solutions, third party software can be the “best of breed” to suit the process and scope at hand, standardised BIM solutions consider the wider implications of information exchange outside the scope of any particular vendor, information can be archived as ASCII files for archival purposes, and data quality can be enhanced as the now single source of users’ information has improved accuracy, correctness, currency, completeness and relevance. SOH current building standards have been successfully drafted for a BIM environment and are confidently expected to be fully developed when BIM is adopted operationally by SOH. There have been remarkably few technical difficulties in converting the House’s existing conventions and standards to the new model based environment. This demonstrates that the IFC model represents world practice for building data representation and management (see Sydney Opera House – FM Exemplar Project Report Number 2005-001-C-3, Open Specification for BIM: Sydney Opera House Case Study). Availability of FM applications based on BIM is in its infancy but focussed systems are already in operation internationally and show excellent prospects for implementation systems at SOH. In addition to the generic benefits of standardised BIM described above, the following FM specific advantages can be expected from this new integrated facilities management environment: faster and more effective processes, controlled whole life costs and environmental data, better customer service, common operational picture for current and strategic planning, visual decision-making and a total ownership cost model. Tests with partial BIM data – provided by several of SOH’s current consultants – show that the creation of a SOH complete model is realistic, but subject to resolution of compliance and detailed functional support by participating software applications. The showcase has demonstrated successfully that IFC based exchange is possible with several common BIM based applications through the creation of a new partial model of the building. Data exchanged has been geometrically accurate (the SOH building structure represents some of the most complex building elements) and supports rich information describing the types of objects, with their properties and relationships.

Investment decision framework for infrastructure asset management: a probability-based approach

Australia’s civil infrastructure assets of roads, bridges, railways, buildings and other structures are worth billions of dollars. Road assets alone are valued at around A$ 140 billion. As the condition of assets deteriorate over time, close to A$10 billion is spent annually in asset maintenance on Australia's roads, or the equivalent of A$27 million per day. To effectively manage road infrastructures, firstly, road agencies need to optimise the expenditure for asset data collection, but at the same time, not jeopardise the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates could be accurately estimated. And finally, the prediction of budgets for maintenance and rehabilitation must provide a certain degree of reliability. A procedure for assessing investment decision for road asset management has been developed. The procedure includes: • A methodology for optimising asset data collection; • A methodology for calibrating deterioration prediction models; • A methodology for assessing risk-adjusted estimates for life-cycle cost estimates. • A decision framework in the form of risk map

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.

Strategy for the development of investment decision-making framework for road asset management for queensland department of main roads

The aim of this project is to develop a systematic investment decision-making framework for infrastructure asset management by incorporation economic justification, social and environmental consideration in the decision-making process. This project assesses the factors that are expected to provide significant impacts on the variability of expenditures. A procedure for assessing risk and reliability for project investment appraisals will be developed. The project investigates public perception, social and environmental impacts on road infrastructure investment. This research will contribute to the debate about how important social and environmental issues should be incorporated into the investment decision-making process for infrastructure asset management.

Review on Australian and international practices for asset management in building infrastructure

Both in developed and developing economies, major public funding is invested in civil infrastructure assets. Efficiency and comfort level of expected and demanded living standards are largely dependant on the management strategies of these assets. Buildings are one of the major & vital assets, which need to be maintained primarily to ensure its functionality by effective & efficient delivery of services and to optimize economic benefits. Not withstanding, public building infrastructure is not considered in Infrastructure report card published by Australian Infrastructure Report Card Alliance Partners (2001). The reason appears to be not having enough data to rate public building infrastructure. American Infrastructure Report Card (2001) gave “School Buildings” ‘d-’ rating, which is below ‘poor’. For effective asset management of building infrastructure, a need emerged to optimise the budget for managing assets, to cope up with increased user expectations, to response effectively to possible asset failures, to deal with ageing of assets and aging populations and to treat other scenarios including technology advancement and non-asset solutions. John (Asset Management, 2001) suggests that in the area of asset management worldwide, UK, Australia and New Zealand are leading.

Investment decision-making under risk (reliability) and uncertainty for infrastructure asset management

Risks and uncertainties are inevitable in engineering projects and infrastructure investments. Decisions about investment in infrastructure such as for maintenance, rehabilitation and construction works can pose risks, and may generate significant impacts on social, cultural, environmental and other related issues. This report presents the results of a literature review of current practice in identifying, quantifying and managing risks and predicting impacts as part of the planning and assessment process for infrastructure investment proposals. In assessing proposals for investment in infrastructure, it is necessary to consider social, cultural and environmental risks and impacts to the overall community, as well as financial risks to the investor. The report defines and explains the concept of risk and uncertainty, and describes the three main methodology approaches to the analysis of risk and uncertainty in investment planning for infrastructure, viz examining a range of scenarios or options, sensitivity analysis, and a statistical probability approach, listed here in order of increasing merit and complexity. Forecasts of costs, benefits and community impacts of infrastructure are recognised as central aspects of developing and assessing investment proposals. Increasingly complex modelling techniques are being used for investment evaluation. The literature review identified forecasting errors as the major cause of risk. The report contains a summary of the broad nature of decision-making tools used by governments and other organisations in Australia, New Zealand, Europe and North America, and shows their overall approach to risk assessment in assessing public infrastructure proposals. While there are established techniques to quantify financial and economic risks, quantification is far less developed for political, social and environmental risks and impacts. The report contains a summary of the broad nature of decision-making tools used by governments and other organisations in Australia, New Zealand, Europe and North America, and shows their overall approach to risk assessment in assessing public infrastructure proposals. While there are established techniques to quantify financial and economic risks, quantification is far less developed for political, social and environmental risks and impacts. For risks that cannot be readily quantified, assessment techniques commonly include classification or rating systems for likelihood and consequence. The report outlines the system used by the Australian Defence Organisation and in the Australian Standard on risk management. After each risk is identified and quantified or rated, consideration can be given to reducing the risk, and managing any remaining risk as part of the scope of the project. The literature review identified use of risk mapping techniques by a North American chemical company and by the Australian Defence Organisation. This literature review has enabled a risk assessment strategy to be developed, and will underpin an examination of the feasibility of developing a risk assessment capability using a probability approach.