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.

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.

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.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

Indoor environments : design, productivity and health

In a typical large office block, by far the largest lifetime expense is the salaries of the workers - 84% for salaries compared with : office rent (14%), total energy (1%), and maintenance (1%). The key drive for business is therefore the maximisation of the productivity of the employees as this is the largest cost. Reducing total energy use by 50% will not produce the same financial return as 1% productivity improvement? The aim of the project which led to this review of the literature was to understand as far as possible the state of knowledge internationally about how the indoor environment of buildings does influence occupants and the impact this influence may have on the total cost of ownership of buildings. Therefore one of the main focus areas for the literature has been identifying whether there is a link between productivity and health of building occupants and the indoor environment. Productivity is both easy to define - the ratio of output to input - but at the same time very hard to measure in a relatively small environment where individual contributions can influence the results, in particular social interactions. Health impacts from a building environment are also difficult to measure well, as establishing casual links between the indoor environment and a particular health issue can be very difficult. All of those issues are canvassed in the literature reported here. Humans are surprisingly adaptive to different physical environments, but the workplace should not test the limits of human adaptability. Physiological models of stress, for example, accept that the body has a finite amount of adaptive energy available to cope with stress. The importance of, and this projects' focus on, the physical setting within the integrated system of high performance workplaces, means this literature survey explores research which has been undertaken on both physical and social aspects of the built environment. The literature has been largely classified in several different ways, according to the classification scheme shown below. There is still some inconsistency in the use of keywords, which is being addressed and greater uniformity will be developed for a CD version of this literature, enabling searching using this classification scheme.

A review of LCADesign

Objective The review addresses two distinct sets of issues: 1. specific functionality, interface, and calculation problems that presumably can be fixed or improved; and 2. the more fundamental question of whether the system is close to being ready for ‘commercial prime time’ in the North American market. Findings Many of our comments relate to the first set of issues, especially sections B and C. Sections D and E deal with the second set. Overall, we feel that LCADesign represents a very impressive step forward in the ongoing quest to link CAD with LCA tools and, more importantly, to link the world of architectural practice and that of environmental research. From that perspective, it deserves continued financial support as a research project. However, if the decision is whether or not to continue the development program from a purely commercial perspective, we are less bullish. In terms of the North American market, there are no regulatory or other drivers to press design teams to use a tool of this nature. There is certainly interest in this area, but the tools must be very easy to use with little or no training. Understanding the results is as important in this regard as knowing how to apply the tool. Our comments are fairly negative when it comes to that aspect. Our opinion might change to some degree when the ‘fixes’ are made and the functionality improved. However, as discussed in more detail in the following sections, we feel that the multi-step process — CAD to IFC to LCADesign — could pose a serious problem in terms of market acceptance. The CAD to IFC part is impossible for us to judge with the information provided, and we can’t even begin to answer the question about the ease of using the software to import designs, but it appears cumbersome from what we do know. There does appear to be a developing North American market for 3D CAD, with a recent survey indicating that about 50% of the firms use some form of 3D modeling for about 75% of their projects. However, this does not mean that full 3D CAD is always being used. Our information suggests that AutoDesk accounts for about 75 to 80% of the 3D CAD market, and they are very cautious about any links that do not serve a latent demand. Finally, other system that link CAD to energy simulation are using XML data transfer protocols rather than IFC files, and it is our understanding that the market served by AutoDesk tends in that direction right now. This is a subject that is outside our area of expertise, so please take these comments as suggestions for more intensive market research rather than as definitive findings.

Environmental assessment for commercial buildings: Stakeholder requirements and tool characteristics

The Cooperative Research Centre for Construction Innovation (CRC CI) is a national research, development and implementation centre focused on the needs of the property, design, construction and facility management sectors. Established in 2001 and headquartered at Queensland University of Technology as an unincorporated joint venture under the Australian Government's Cooperative Research Program, the CRC CI is developing key technologies, tools and management systems to improve the effectiveness of the construction industry. The CRC CI is a seven year project funded by a Commonwealth grant and industry, research and other government support. More than 150 researchers and an alliance of 19 leading partner organisations are involved in and support the activities of the CRC CI

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

Sustainability and the building code of Australia

This report is the culmination of a two-stage research project to inform the Australian property and construction industry generally, in addition to providing the Australian Building Codes Board (ABCB) with information to allow it to determine whether or not sustainability requirements are necessary in the Future Building Code of Australia (BCA21). The Australian Building Codes Board is a joint initiative of all levels of government in Australia. The Board’s mission is to provide for efficiency and cost effectiveness in meeting community expectations for health, safety and amenity in the design, construction and use of buildings through the creation of nationally consistent building codes, standards, regulatory requirements and regulatory systems. The Stage 1 (literature review) and Stage 2 (workshops) reports are intended to be read in conjunction with one another. These reports and the Database are provided as appendices. The Conclusions of this, the final report, are the result of the overall program of work.

Final report of Delphi study

This is the final report of project 2002-010 Component Life – A Delphi Approach to Life Prediction of Building Material Components. A Delphi survey has been conducted to provide expert opinion on the life of components in buildings. Thirty different components were surveyed with a range of materials, coatings, environments and failure considered. These components were chosen to be representative of a wider range of components in the same building microclimate. The survey included both service life (with and without maintenance) and aesthetic life, and time to first maintenance. It included marine, industrial, and benign environments, and covered both commercial and residential buildings. In order to obtain answers to this wide range of question, but still have a survey that could be completed in a reasonable time, the survey was broken into five sections: 1 External metal components – residential buildings. 2. Internal metal components – residential buildings. 3. External metal components – commercial buildings. 4. Internal metal components – commercial buildings. 5. Metal connectors in buildings.