Improving ecological performance of industrialized building systems in Malaysia

For construction stakeholders to fully embrace sustainability, its long-term benefits and associated risks need to be identified through holistic approaches. Consensus among key stakeholders is very important to the improvement of the ecological performance of industrialized building systems (IBS), a building construction method gaining momentum in Malaysia. A questionnaire survey examines the relative significance of 16 potentially important sustainability factors for IBS applications. To present possible solutions,semi-structured interviews solicit views from experienced IBS practitioners, representing the professions involved. Three most critical factors agreed by key stakeholders are material consumption, waste generation and waste disposal. Using SWOT analysis, the positive and negative aspects of these factors are investigated, with action plans formulated for IBS design practitioners. The SWOT analysis based guidelines have the potential to become part of IBS design briefing documents against which sustainability solutions are contemplated, selected and implemented. Existing knowledge on ecological performance issues is extended by considering the unique characteristics of IBS and identifying not only the benefits, but also the potential risks and challenges of pursuing sustainability. This is largely missing in previous research efforts. Findings to date focus on providing much-needed assistance to IBS designers, who are at the forefront of decision-making with a significant level of project influence. Ongoing work will be directed towards other project development phases and consider the inherent linkage between design decisions and subsequent sustainability deliverables in the project life cycle.

Decision making guidelines for sustainable construction of industrialised building systems

The construction industry has an obligation to respond to sustainability expectations of our society. Solutions that integrate innovative, intelligent and sustainability deliverables are vital for us to meet new and emerging challenges. Industrialised Building Systems (IBS), or known otherwise as prefabrication, employs a combination of ready-made components in the construction of buildings. They promote quality of production, enhance simplification of construction processes and minimise waste. The unique characteristics of this construction method respond well to sustainability. Despite the promises however, IBS has yet to be effectively implemented in Malaysia. There are often misconceptions among key stakeholders about IBS applications. The existing rating schemes fail to assess IBS against sustainability measures. To ensure the capture of full sustainability potential in buildings developed, the critical factors and action plans agreeable to all participants in the development processes need to be identified. Through questionnaire survey, eighteen critical factors relevant to IBS sustainability were identified and encapsulated into a conceptual framework to coordinate a systematic IBS decision making approach. Five categories were used to separate the critical factors into: ecological performance; economic value; social equity and culture; technical quality; and implementation and enforcement. This categorisation extends the "Triple Bottom Lines" to include social, economic, environmental and institutional dimensions. Semi-structured interviews help identify strategies of actions and solutions of potential problems through a SWOT analysis framework. These tools help the decision-makers maximise the opportunities by using available strengths, avoid weaknesses, and diagnose possible threats in the examined issues. The recommendations formed an integrated action plan to present information on what and how to improve sustainability through tackling each critical factor during IBS development. It can be used as part of the project briefing documents for IBS designers. For validation and finalisation the research deliverables, three case studies were conducted. The research fills a current gap by responding to IBS project scenarios in developing countries. It also provides a balanced view for designers to better understand sustainability potential and prioritize attentions to manage sustainability issues in IBS applications.

Classified information nondisclosure agreement (standard form 312) : briefing booklet.

Shipping list no.: 89-380-P.

Information security : actions taken to improve Lockheed's special access document accountability : briefing report to the Chairman, Subcommittee on Oversight and Investigations, Committee on Energy and Commerce, House of Representatives /

"B-223830"--p.[1]

PROPERTY TAX BRIEFING PRESENTED TO SOUTH CAROLINA SENATE FINANCE COMMITTEE

This Powerpoint presentation gives statistics on property taxes in South Carolina.

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.

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.

HCX : an efficient hybrid clustering approach for XML documents

This paper proposes a novel Hybrid Clustering approach for XML documents (HCX) that first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. The empirical analysis reveals that the proposed method is scalable and accurate.

XCFS - a novel approach for clustering XML documents using both the structure and the content

XML document clustering is essential for many document handling applications such as information storage, retrieval, integration and transformation. An XML clustering algorithm should process both the structural and the content information of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. This paper introduces a novel approach that first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. The proposed method reduces the high dimensionality of input data by using only the structure-constrained content. The empirical analysis reveals that the proposed method can effectively cluster even very large XML datasets and outperform other existing methods.

Practice briefing : Is BOT the best financing model to procure infrastructure projects? A case study of the Hong Kong-Shuhai-Macau Bridge

Abstract: Purpose – Several major infrastructure projects in the Hong Kong Special Administrative Region (HKSAR) have been delivered by the build-operate-transfer (BOT) model since the 1960s. Although the benefits of using BOT have been reported abundantly in the contemporary literature, some BOT projects were less successful than the others. This paper aims to find out why this is so and to explore whether BOT is the best financing model to procure major infrastructure projects. Design/methodology/approach – The benefits of BOT will first be reviewed. Some completed BOT projects in Hong Kong will be examined to ascertain how far the perceived benefits of BOT have been materialized in these projects. A highly profiled project, the Hong Kong-Zhuhai-Macau Bridge, which has long been promoted by the governments of the People's Republic of China, Macau Special Administrative Region and the HKSAR that BOT is the preferred financing model, but suddenly reverted back to the traditional financing model to be funded primarily by the three governments with public money instead, will be studied to explore the true value of the BOT financial model. Findings – Six main reasons for this radical change are derived from the analysis: shorter take-off time for the project; difference in legal systems causing difficulties in drafting BOT agreements; more government control on tolls; private sector uninterested due to unattractive economic package; avoid allegation of collusion between business and the governments; and a comfortable financial reserve possessed by the host governments. Originality/value – The findings from this paper are believed to provide a better understanding to the real benefits of BOT and the governments' main decision criteria in delivering major infrastructure projects.

Effective pattern taxonomy mining in text documents

Many data mining techniques have been proposed for mining useful patterns in databases. However, how to effectively utilize discovered patterns is still an open research issue, especially in the domain of text mining. Most existing methods adopt term-based approaches. However, they all suffer from the problems of polysemy and synonymy. This paper presents an innovative technique, pattern taxonomy mining, to improve the effectiveness of using discovered patterns for finding useful information. Substantial experiments on RCV1 demonstrate that the proposed solution achieves encouraging performance.