Final Report : learning system for life prediction of infrastructure

The project has further developed two programs for the industry partners related to service life prediction and salt deposition. The program for Queensland Department of Main Roads which predicts salt deposition on different bridge structures at any point in Queensland has been further refined by looking at more variables. It was found that the height of the bridge significantly affects the salt deposition levels only when very close to the coast. However the effect of natural cleaning of salt by rainfall was incorporated into the program. The user interface allows selection of a location in Queensland, followed by a bridge component. The program then predicts the annual salt deposition rate and rates the likely severity of the environment. The service life prediction program for the Queensland Department of Public Works has been expanded to include 10 common building components, in a variety of environments. Data mining procedures have been used to develop the program and increase the usefulness of the application. A Query Based Learning System (QBLS) has been developed which is based on a data-centric model with extensions to provide support for user interaction. The program is based on number of sources of information about the service life of building components. These include the Delphi survey, the CSIRO Holistic model and a school survey. During the project, the Holistic model was modified for each building component and databases generated for the locations of all Queensland schools. Experiments were carried out to verify and provide parameters for the modelling. These included instrumentation of a downpipe, measurements on pH and chloride levels in leaf litter, EIS measurements and chromate leaching from Colorbond materials and dose tests to measure corrosion rates of new materials. A further database was also generated for inclusion in the program through a large school survey. Over 30 schools in a range of environments from tropical coastal to temperate inland were visited and the condition of the building components rated on a scale of 0-5. The data was analysed and used to calculate an average service life for each component/material combination in the environments, where sufficient examples were available.

Final report : feasibility study linking best value procurement assessment to outcome performance indicators

This feasibility study was established to investigate the application of the concept of ‘best value’ in construction procurement in Australia. In the case of ‘best value’ in the business enterprise, ‘best value’ is that which returns greatest value to the business enterprise’s shareholders. However, in the case of the public sector, ‘best value’ is more complex. For that reason, this research project focuses mainly on public sector construction project procurement.

Best value second interim report

This first interim report on ‘best value’ reviewed the academic literature relating to ‘best value’ and illustrated the varying interpretations that the concept of ‘best value’ has attracted. It also examined current state of the literature on best value in construction project procurement. ‘Best value’ was explored from the perspective of both the business enterprise and public sector. It was concluded that ‘best value’ in the public sector is considerably more complex. This second interim report explores how ‘best value’ has been implemented in the UK. Focus is directed towards the UK, particularly Scotland’s approach to examine the complexity of implementation of ‘best value’ in the public sector context. Scotland has been recognised as a leader in the field of ‘best value’ in the public sector (Curry, 1999; Wisniewski and Stewart, 2001, 2004; Jaconelli and Sheffield, 2000)

Report : stochastic analysis of road asset data for maintenance cost prediction

Reliable budget/cost estimates for road maintenance and rehabilitation are subjected to uncertainties and variability in road asset condition and characteristics of road users. The CRC CI research project 2003-029-C ‘Maintenance Cost Prediction for Road’ developed a method for assessing variation and reliability in budget/cost estimates for road maintenance and rehabilitation. The method is based on probability-based reliable theory and statistical method. The next stage of the current project is to apply the developed method to predict maintenance/rehabilitation budgets/costs of large networks for strategic investment. The first task is to assess the variability of road data. This report presents initial results of the analysis in assessing the variability of road data. A case study of the analysis for dry non reactive soil is presented to demonstrate the concept in analysing the variability of road data for large road networks. In assessing the variability of road data, large road networks were categorised into categories with common characteristics according to soil and climatic conditions, pavement conditions, pavement types, surface types and annual average daily traffic. The probability distributions, statistical means, and standard deviation values of asset conditions and annual average daily traffic for each type were quantified. The probability distributions and the statistical information obtained in this analysis will be used to asset the variation and reliability in budget/cost estimates in later stage. Generally, we usually used mean values of asset data of each category as input values for investment analysis. The variability of asset data in each category is not taken into account. This analysis method demonstrated that it can be used for practical application taking into account the variability of road data in analysing large road networks for maintenance/rehabilitation investment analysis.

The user query based learning system for lifetime prediction of metallic components

Real-World Data Mining Applications generally do not end up with the creation of the models. The use of the model is the final purpose especially in prediction tasks. The problem arises when the model is built based on much more information than that the user can provide in using the model. As a result, the performance of model reduces drastically due to many missing attributes values. This paper develops a new learning system framework, called as User Query Based Learning System (UQBLS), for building data mining models best suitable for users use. We demonstrate its deployment in a real-world application of the lifetime prediction of metallic components in buildings

What value do users derive from social network applications?

Research on social networking sites like Facebook is emerging but sparse. This exploratory study investigates the value users derive from self-described ‘cool’ Facebook applications, and explores the features that either encourage or discourage users to recommend applications to their friends. The concepts of value and cool are explored in a social networking context. Our qualitative data reveals consumers derive a combination of functional value along with either social or emotional value from the applications. Female Facebook users indicate self-expression as important motivators, while males tend to use Facebook applications to socially compete. Three broad categories emerged for application features; symmetrical features can both encourage or discourage recommendation, polar features where different levels of the same feature encourage or discourage, and uni-directional features only encourage or discourage but not both. Recommending or not recommending an application tends to be the result of a combination of features and context, rather than one feature in isolation.

The journey to delivered value in Australian procurement

In line with developments overseas Australian clients are turning to considerations of value in project procurement. Until the 1980s the industry operated in a largely traditional manner however the extremely adversarial behaviour exhibited during towards the end of the decade led to a number of significant events and initiatives including the publication of “No Dispute”, the Gyles Royal Commission into the Building Industry, the Construction Industry Development Agency (CIDA) and the work of the Australian Procurement and Construction Council (APCC). A number of research projects in progress in the CRC for Construction Innovation (CRC CI) are focussing on the assessment of value and methodologies to support the delivery of value in the procurement and management of engineering and construction projects. This paper charts the emergence of several key drivers in the process and illustrates how they can be integrated into a comprehensive Decision Support System that balances value to stakeholders with project imperatives and incorporates a lessons learned data base which enriches the decision making process to optimise delivery method design and selection.

Achieving value alignment in project delivery

A study into best practice project delivery and the development of a suite of products, resources and services to help guide clients and project teams towards the best approach for specific projects.

Bayesian model averaging for harmful algal bloom prediction

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.

Conceptualising value creation for social change management

Australian and international governments are increasingly adopting social marketing as a social change management tool to deal with complex social problems. Government decision makers typically need to balance the use of business models and management theories whilst maintaining the integrity of government policy. In taking this approach, decision makers experience management tensions between a social mission to equitably deliver social services and the accountability and affordability of providing quality social and health services to citizens. This is a significant challenge as the nature of the ‘social product’ in government is often more service-oriented than goods-based. In this paper the authors examine value creation in government social marketing services. The contribution of this paper is a value creation process model, which considers the nature of governments to create social good. This is particularly important for governments where consumers still expect value and quality in the service delivered, despite that offering being ‘free’.

Prediction of highway truck conveyance configuration under over-limit prohibition and conveyance equilibrium

Aiming at the shortage of prevailing prediction methods about highway truck conveyance configuration in over-limit freight research that transferring the goods attributed to over-limit portion to another fully loaded truck of the same configuration and developing the truck traffic volume synchronously, a new way to get accumulated probability function of truck power tonnage in basal year by highway truck classified by wheel and axle type load mass spectrum investigation was presented. Logit models were used to forecast overall highway freight diversion and single cargo tonnage diversion when the weight rules and strict of enforcement intensity of overload were changed in scheme year. Assumption that the probability distribution of single truck loadage should be consistent with the probability distribution of single goods freighted, the model describes the truck conveyance configuration in the future under strict over-limit prohibition. The model was used and tested in Highway Over-limit Research Project in Anhui by World Bank.

Membership retention within the sporting industry : factors affecting relationship dissolution

Relationship dissolution has been somewhat ignored in the study of relationship marketing paradigm. While there has been an abundance of literature giving broad conceptualizations on how to master the intricacies of relationships, very little has discussed the concept of relationship dissolution. This is especially true of the sporting industry, which does not yet understand the factors that contribute to members relinquishing their membership and severing relationship ties with the club. Team performance was found to be the most powerful predictor of relationship dissolution; however, both satisfaction with the sportscape and emotional bonds had a significant influence on the decision for a member not to renew their membership. Although team performance is mostly out of the hands of sport marketers, greater focus should be given to implementing strategies that enhance the emotional aspects of the club-member relationship while also improving aspects of the service facility.

A holistic approach to the support and engagement of first year students : a retention strategy

Institutions should enact holistic approaches that address students’ personal, social and academic engagement in the early weeks of first year to facilitate retention (Nelson, Kift & Clarke, 2008). This holistic approach is central to the FYE program at Queensland University of Technology (QUT), which was established to maximise learning engagement and hence positively influence the retention of commencing students. The program aims to • engage students in their learning through an intentionally designed and enacted curriculum (Kift, 2008) • facilitate timely access to life and learning support • promote a sense of belonging to the discipline, cohort and profession. The FYE program’s aims are achieved by strategic alliances between academic and professional staff across the institution.