939 resultados para FREQUENCY APPROACH
Resumo:
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.
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Although the benefits of service orientation are prevalent in literature, a review, analysis, and evaluation of the 30 existing service analysis approaches presented in this paper have shown that a comprehensive approach to the identification and analysis of both business and supporting software services is missing. Based on this evaluation of existing approaches and additional sources, we close this gap by proposing an integrated, consolidated approach to business and software service analysis that combines and extends the strengths of the examined methodologies.
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An important aspect of designing any product is validation. Virtual design process (VDP) is an alternative to hardware prototyping in which analysis of designs can be done without manufacturing physical samples. In recent years, VDP have been generated either for animation or filming applications. This paper proposes a virtual reality design process model on one of the applications when used as a validation tool. This technique is used to generate a complete design guideline and validation tool of product design. To support the design process of a product, a virtual environment and VDP method were developed that supports validation and an initial design cycle performed by a designer. The product model car carrier is used as illustration for which virtual design was generated. The loading and unloading sequence of the model for the prototype was generated using automated reasoning techniques and was completed by interactively animating the product in the virtual environment before complete design was built. By using the VDP process critical issues like loading, unloading, Australian Design rules (ADR) and clearance analysis were done. The process would save time, money in physical sampling and to large extent in complete math generation. Since only schematic models are required, it saves time in math modelling and handling of bigger size assemblies due to complexity of the models. This extension of VDP process for design evaluation is unique and was developed, implemented successfully. In this paper a Toll logistics and J Smith and Sons car carrier which is developed under author’s responsibility has been used to illustrate our approach of generating design validation via VDP.
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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
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Over the past twenty years brand loyalty has been an important topic for both marketing practitioners and academics. While practitioners have produced proprietary brand loyalty audit models, there has been little academic research to make transparent the methodology that underpins these audits and to enable practitioners to understand, develop and conduct their own audits. In this paper, we propose a framework for a brand loyalty audit that uses a tri-dimensional approach to brand loyalty, which includes behavioural loyalty and the two components of attitudinal loyalty: emotional and cognitive loyalty. In allowing for different levels and intensity of brand loyalty, this tri-dimensional approach is important from a managerial perspective. It means that loyalty strategies that arise from a brand audit can be made more effective by targeting the market segments that demonstrate the most appropriate combination of brand loyalty components. We propose a matrix with three dimensions (emotional, cognitive and behavioural loyalty) and two levels (high and low loyalty) to facilitate a brand loyalty audit. To demonstrate this matrix, we use the example of financial services, in particular a rewards-based credit card.
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A recent Australian survey of beginning teachers indicates that issue of classroom management continues to be a key concern for early career educators (Australian Education Union, 2007). This finding is supported by the wider literature that identifies managing the classroom, particularly managing behaviour within the classroom, as critical issues for early career teachers (Arends, 2006; Charles, 2004; Groundwater-Smith, Ewing & Le Cornu, 2007). In fact, struggling to manage student behaviour and maintain positive relationships with students are among the top reasons for teachers leaving the teaching profession (Charles, 2004). So, how does a teacher effectively organise and manage up to thirty students learning and behaviour at any one time? The issue of classroom management is a persistent one for all teachers, but is particularly daunting for new teachers. Historically, classrooms were established on strong hierarchical structures that relied heavily on teacher control and authority. However, more recent approaches to managing the classroom are proactive and more collaborative. That is not to say that there exists a single management recipe, far from it. Beginning teachers must view possible approaches to managing the classroom in light of their own beliefs about teaching and learning, their current classroom practice and variables from the context in which they are teaching.
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The endeavour to obtain estimates of durability of components for use in lifecycle assessment or costing and infrastructure and maintenance planning systems is large. The factor method and the reference service life concept provide a very valuable structure, but do not resolve the central dilemma of the need to derive an extensive database of service life. Traditional methods of estimating service life, such as dose functions or degradation models, can play a role in developing this database, however the scale of the problem clearly indicates that individual dose functions cannot be derived for each component in each different local and geographic setting. Thus, a wider range of techniques is required in order to devise reference service life. This paper outlines the approaches being taken in the Cooperative Research Centre for Construction Innovation project to predict reference service life. Approaches include the development of fundamental degradation and microclimate models, the development of a situation-based reasoning ‘engine’ to vary the ‘estimator’ of service life, and the development of a database on expert performance (Delphi study). These methods should be viewed as complementary rather than as discrete alternatives. As discussed in the paper, the situation-based reasoning approach in fact has the possibility of encompassing all other methods.
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Many factors have the potential to influence human health. These factors need to be monitored to maintain health. As is the case with human health, construction projects have a number of critical factors that can facilitate a broad evaluation of project health. In order to use these factors as an indication of health, they need to be assessed. This assessment can help to achieve desired outcomes for the project. This paper discusses the approach of assessing Critical Success Factors (CSFs) using Key Performance Indicators (KPIs) to ascertain the immediate health of a construction project. This approach is applicable to all phases of construction projects and many construction procurement methods. KPIs have been benchmarked on the basis of industry standards and historical data. The robustness of the KPIs to assess the immediate health of a project has been validated using Australian and international case studies.
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Buildings consume resources and energy, contribute to pollution of our air, water and soil, impact the health and well-being of populations and constitute an important part of the built environment in which we live. The ability to assess their design with a view to reducing that impact automatically from their 3D CAD representations enables building design professionals to make informed decisions on the environmental impact of building structures. Contemporary 3D object-oriented CAD files contain a wealth of building information. LCADesign has been designed as a fully integrated approach for automated eco-efficiency assessment of commercial buildings direct from 3D CAD. LCADesign accesses the 3D CAD detail through Industry Foundation Classes (IFCs) - the international standard file format for defining architectural and constructional CAD graphic data as 3D real-world objects - to permit construction professionals to interrogate these intelligent drawing objects for analysis of the performance of a design. The automated take-off provides quantities of all building components whose specific production processes, logistics and raw material inputs, where necessary, are identified to calculate a complete list of quantities for all products such as concrete, steel, timber, plastic etc and combines this information with the life cycle inventory database, to estimate key internationally recognised environmental indicators such as CML, EPS and Eco-indicator 99. This paper outlines the key modules of LCADesign and their role in delivering an automated eco-efficiency assessment for commercial buildings.
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Construction projects are faced with a challenge that must not be underestimated. These projects are increasingly becoming highly competitive, more complex, and difficult to manage. They become ‘wicked problems’, which are difficult to solve using traditional approaches. Soft Systems Methodology (SSM) is a systems approach that is used for analysis and problem solving in such complex and messy situations. SSM uses “systems thinking” in a cycle of action research, learning and reflection to help understand the various perceptions that exist in the minds of the different people involved in the situation. This paper examines the benefits of applying SSM to wicked problems in construction project management, especially those situations that are challenging to understand and difficult to act upon. It includes relevant examples of its use in dealing with the confusing situations that incorporate human, organizational and technical aspects.
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Harmful Algal Blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been developed, and in common with most predictive models in ecology, those that have been developed do not fully account for uncertainties in parameters and model structure. This makes management decisions based on these predictions more risky than might be supposed. We used a probit time series model and Bayesian Model Averaging (BMA) to predict occurrences of blooms of Lyngbya majuscula, a toxic cyanophyte, in Deception Bay, Queensland, Australia. We found a suite of useful predictors for HAB occurrence, with Temperature figuring prominently in models with the majority of posterior support, and a model consisting of the single covariate average monthly minimum temperature showed by far the greatest posterior support. A comparison of alternative model averaging strategies was made with one strategy using the full posterior distribution and a simpler approach that utilised the majority of the posterior distribution for predictions but with vastly fewer models. Both BMA approaches showed excellent predictive performance with little difference in their predictive capacity. Applications of BMA are still rare in ecology, particularly in management settings. This study demonstrates the power of BMA as an important management tool that is capable of high predictive performance while fully accounting for both parameter and model uncertainty.
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The current policy decision making in Australia regarding non-health public investments (for example, transport/housing/social welfare programmes) does not quantify health benefits and costs systematically. To address this knowledge gap, this study proposes an economic model for quantifying health impacts of public policies in terms of dollar value. The intention is to enable policy-makers in conducting economic evaluation of health effects of non-health policies and in implementing policies those reduce health inequalities as well as enhance positive health gains of the target population. Health Impact Assessment (HIA) provides an appropriate framework for this study since HIA assesses the beneficial and adverse effects of a programme/policy on public health and on health inequalities through the distribution of those effects. However, HIA usually tries to influence the decision making process using its scientific findings, mostly epidemiological and toxicological evidence. In reality, this evidence can not establish causal links between policy and health impacts since it can not explain how an individual or a community reacts to changing circumstances. The proposed economic model addresses this health-policy linkage using a consumer choice approach that can explain changes in group and individual behaviour in a given economic set up. The economic model suggested in this paper links epidemiological findings with economic analysis to estimate the health costs and benefits of public investment policies. That is, estimating dollar impacts when health status of the exposed population group changes by public programmes – for example, transport initiatives to reduce congestion by building new roads/ highways/ tunnels etc. or by imposing congestion taxes. For policy evaluation purposes, the model is incorporated in the HIA framework by establishing association among identified factors, which drive changes in the behaviour of target population group and in turn, in the health outcomes. The economic variables identified to estimate the health inequality and health costs are levels of income, unemployment, education, age groups, disadvantaged population groups, mortality/morbidity etc. However, though the model validation using case studies and/or available database from Australian non-health policy (say, transport) arena is in the future tasks agenda, it is beyond the scope of this current paper.
Resumo:
There is a growing evidence-base in the epidemiological literature that demonstrates significant associations between people’s living circumstances – including their place of residence – and their health-related practices and outcomes (Leslie, 2005; Karpati, Bassett, & McCord, 2006; Monden, Van Lenthe, & Mackenbach, 2006; Parkes & Kearns, 2006; Cummins, Curtis, Diez-Roux, & Macintyre, 2007; Turrell, Kavanagh, Draper, & Subramanian, 2007). However, these findings raise questions about the ways in which living places, such as households and neighbourhoods, figure in the pathways connecting people and health (Frolich, Potvin, Chabot, & Corin, 2002; Giles-Corti, 2006; Brown et al, 2006; Diez Roux, 2007). This thesis addressed these questions via a mixed methods investigation of the patterns and processes connecting people, place, and their propensity to be physically active. Specifically, the research in this thesis examines a group of lower-socioeconomic residents who had recently relocated from poorer suburbs to a new urban village with a range of health-related resources. Importantly, the study contrasts their historical relationship with physical activity with their reactions to, and everyday practices in, a new urban setting designed to encourage pedestrian mobility and autonomy. The study applies a phenomenological approach to understanding living contexts based on Berger and Luckman’s (1966) conceptual framework in The Social Construction of Reality. This framework enables a questioning of the concept of context itself, and a treatment of it beyond environmental factors to the processes via which experiences and interactions are made meaningful. This approach makes reference to people’s histories, habituations, and dispositions in an exploration between social contexts and human behaviour. This framework for thinking about context is used to generate an empirical focus on the ways in which this residential group interacts with various living contexts over time to create a particular construction of physical activity in their lives. A methodological approach suited to this thinking was found in Charmaz’s (1996; 2001; 2006) adoption of a social constructionist approach to grounded theory. This approach enabled a focus on people’s own constructions and versions of their experiences through a rigorous inductive method, which provided a systematic strategy for identifying patterns in the data. The findings of the study point to factors such as ‘childhood abuse and neglect’, ‘early homelessness’, ‘fear and mistrust’, ‘staying indoors and keeping to yourself’, ‘conflict and violence’, and ‘feeling fat and ugly’ as contributors to an ongoing core category of ‘identity management’, which mediates the relationship between participants’ living contexts and their physical activity levels. It identifies barriers at the individual, neighbourhood, and broader ecological levels that prevent this residential group from being more physically active, and which contribute to the ways in which they think about, or conceptualise, this health-related behaviour in relationship to their identity and sense of place – both geographic and societal. The challenges of living well and staying active in poorer neighbourhoods and in places where poverty is concentrated were highlighted in detail by participants. Participants’ reactions to the new urban neighbourhood, and the depth of their engagement with the resources present, are revealed in the context of their previous life-experiences with both living places and physical activity. Moreover, an understanding of context as participants’ psychological constructions of various social and living situations based on prior experience, attitudes, and beliefs was formulated with implications for how the relationship between socioeconomic contextual effects on health are studied in the future. More detailed findings are presented in three published papers with implications for health promotion, urban design, and health inequalities research. This thesis makes a substantive, conceptual, and methodological contribution to future research efforts interested in how physical activity is conceptualised and constructed within lower socioeconomic living contexts, and why this is. The data that was collected and analysed for this PhD generates knowledge about the psychosocial processes and mechanisms behind the patterns observed in epidemiological research regarding socioeconomic health inequalities. Further, it highlights the ways in which lower socioeconomic living contexts tend to shape dispositions, attitudes, and lifestyles, ultimately resulting in worse health and life chances for those who occupy them.