A framework for analyzing system intelligence of the building control system : a study of the integrated building management system

The lack of satisfactory consensus for characterizing the system intelligence and structured analytical decision models has inhibited the developers and practitioners to understand and configure optimum intelligent building systems in a fully informed manner. So far, little research has been conducted in this aspect. This research is designed to identify the key intelligent indicators, and develop analytical models for computing the system intelligence score of smart building system in the intelligent building. The integrated building management system (IBMS) was used as an illustrative example to present a framework. The models presented in this study applied the system intelligence theory, and the conceptual analytical framework. A total of 16 key intelligent indicators were first identified from a general survey. Then, two multi-criteria decision making (MCDM) approaches, the analytic hierarchy process (AHP) and analytic network process (ANP), were employed to develop the system intelligence analytical models. Top intelligence indicators of IBMS include: self-diagnostic of operation deviations; adaptive limiting control algorithm; and, year-round time schedule performance. The developed conceptual framework was then transformed to the practical model. The effectiveness of the practical model was evaluated by means of expert validation. The main contribution of this research is to promote understanding of the intelligent indicators, and to set the foundation for a systemic framework that provide developers and building stakeholders a consolidated inclusive tool for the system intelligence evaluation of the proposed components design configurations.

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.

Report on relationship management and culture change

Brisbane Water (BW), a commercialised business arm of Brisbane City Council (BCC) entered into an alliance with a number of organisations from the private sector in order to design, construct, commission and undertake upgrades to three existing wastewater treatment plants located at Sandgate, Oxley Creek, and Wacol in Brisbane. The alliance project is called the Brisbane Water Environmental Alliance (BWEA). This report details the efforts of a team of researchers from the School of Management at Queensland University of Technology to investigate this alliance. This is the second report on this project, and is called Stage 2 of the research. At the time that Stage 2 of the research project was conducted, the BWEA project was nearing completion with a further 8 months remaining before project completion. The aim of this report is to explore individuals’ perceptions of the effectiveness and functioning of the BWEA project in the latter stages of the project. The second aim of this report is to analyse the longitudinal findings of this research project by integrating the findings from Stage 1 and Stage 2 of the project. This long-term analysis of the functioning and effectiveness of the alliance is important because at the current time, researchers have little knowledge of the group developmental processes that occur in large-scale alliances over time. Stage 2 of this research project has a number of aims including assessing performance of the BWEA project from the point of view of a range of stakeholders including the alliance board and alliance management team, alliance staff, and key stakeholders from the client organisation (Brisbane Water). Data were collected using semi-structured interviews with 18 individuals including two board members, one external facilitator, and four staff members from the client organisation. Analysis involved coding the interview transcripts in terms of the major issues that were reported by interviewees.

Systematising knowledge management in projects

Purpose – The purpose of this paper is to provide a practicable systems-based approach to knowledge management (KM) in a project environment, to encourage organisations to unlock the value in their review processes. It relies on knowledge capture and storage at decision review points, to enrich individual, team and organisational learning during the project life cycle. The project's phases are typically represented horizontally with deliverables (objectives) or project "promises" as the desirable outcomes. The purpose of this paper is to give expression through introducing a vertical dimension to facilitate the KM process. A model is proposed that conceptualises project-specific knowledge drawing on and feeding into the organisation's knowledge management system (KMS) at tactical and strategic levels. Design/methodology/approach – This conceptual paper links concepts from systems theory with KM, to produce a model to identify, collate, and optimise project-based knowledge and integrate it into the management process. Findings – The application of the system theory approach enriches the knowledge generated by a project, and feeds it into the next phase of that project. At the same time, it contributes to the individual's and project team's KM, specifies possible courses of action, together with risks, costs and benefits and thus it expands the organisation's higher level KMS. Research limitations/implications – The concept suggests that the knowledge capture, storage and sharing process may best be undertaken holistically, in view of the systems relationships between the tasks. Systems theory structures this process. Research opportunities include studying the interfaces between levels of KM, in relation to the project's progress. Practical implications – Reconceptualisation of the project as a knowledge creation process may improve the project's progress as well as add to the individual's, project team's, and wider organisation's knowledge base. An example is given. Originality/value – This paper illuminates the broader potential of under-utilised opportunities in well-known management approaches to add dimension to the business project, of knowledge creation, storage and sharing.

Re-Lifing commercial buildings : design and construction solutions for existing buildings

The resources listed in this document describe the design and construction opportunities available to building owners who wish to re-Life their properties. They do not yet examine management opportunities, which may also help owners improve the efficiency of their existing stock.

A45 ICT curriculum design to bridge the gap between industry and academia

Building Information Model (BIM) software, collaboration platforms and 5D Construction Management software is now commercially available and presents the opportunity for construction project teams to design more cost effectively, plan construction earlier, manage costs throughout the life cycle of a building project and provide a central asset management register for facilities managers. This paper outlines the merits of taking a holistic view of ICT in curriculum design. The educational barriers to implementation of these models and planning tools are highlighted. Careful choice of computer software can make a significant difference to how quickly students can master skills; how easy it is to study and how much they enjoy learning and be prepared for employment. An argument for BIM and 5D planning tools to be introduced into the curriculum to assist industry increase productivity and efficiencies are outlined by the authors.

Comparison between ACI 440 and FIB 14 design guidelines in using CFRP for strengthening of a concrete bridge headstock

This paper compares and reviews the recommendations and contents of the guide for the design and construction of externally bonded FRP systems for strengthening concrete structures reported by ACI committee 440 and technical report of Externally bonded FRP reinforcement for RC structures (FIB 14) in application of carbon fiber reinforced polymer (CFRP) composites in strengthening of an aging reinforced concrete headstock. The paper also discusses the background, limitations, strengthening for flexure and shear, and other related issues in use of FRP for strengthening of a typical reinforced concrete headstock structure such as durability, de-bonding, strengthening limits, fire and environmental conditions. A case study of strengthening of a bridge headstock using FRP composites is presented as a worked example in order to illustrate and compare the differences between these two design guidelines when used in conjunction with the philosophy of the Austroads (1992) bridge design code.

Adopting BIM for Facilities Adopting BIM for Facilities Management : Solutions for Managing the Sydney Opera House

The digital modelling research stream of the Sydney Opera House FM Exemplar Project has demonstrated significant benefits in digitising design documentation and operational and maintenance manuals. Since Sydney Opera House did not have digital models of its structure, there was an opportunity to investigate the application of digital modelling using standardised Building Information Models (BIM) to support facilities management (FM).The focus of this investigation was on the following areas:the re-usability of standardised BIM for FM purposesthe potential of BIM as an information framework acting as integrator for various FM data sources the extendibility and flexibility of the BIM to cope with business-specific data and requirements commercial FM software using standardised BIMthe ability to add (organisation-specific) intelligence to the modela roadmap for Sydney Opera House to adopt BIM for FM.

What are the lessons for entrepreneurship from creativity and design?

Entrepreneurship and innovation make significant contributions to the success of firms and both notions are often linked directly or indirectly to notions of creativity and more recently design (Drucker, 1985; Kelley, 2001; Nystrom, 1993). This theoretical paper investigates the processes involved in entrepreneurship, creativity and design and identifies key processes relevant to entrepreneurial practice.