Brokering innovation to better leverage R&D investment

What is the contribution of innovation brokers in leveraging research and development (R&D) investment to enhance industry-wide capabilities? The case of the Australian Cooperative Research Centre for Construction Innovation (CRC CI) is considered in the context of motivating supply chain firms to improve their organizational capabilities in order to acquire, assimilate, transfer and exploit R&D outcomes to their advantage, and to create broader industry and national benefits. A previous audit and analysis has shown an increase in business R&D investment since 2001. The role of the CRC CI in contributing to growth in the absorptive capacity of the Australian construction industry as a whole is illustrated through two programmes: digital modelling/building information modelling (BIM) and construction site safety. Numerous positive outcomes in productivity, quality, improved safety and competitiveness were achieved between 2001 and 2009

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

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