Assessing uncertainty in pollutant build-up and wash-off processes

Assessing build-up and wash-off process uncertainty is important for accurate interpretation of model outcomes to facilitate informed decision making for developing effective stormwater pollution mitigation strategies. Uncertainty inherent to pollutant build-up and wash-off processes influences the variations in pollutant loads entrained in stormwater runoff from urban catchments. However, build-up and wash-off predictions from stormwater quality models do not adequately represent such variations due to poor characterisation of the variability of these processes in mathematical models. The changes to the mathematical form of current models with the incorporation of process variability, facilitates accounting for process uncertainty without significantly affecting the model prediction performance. Moreover, the investigation of uncertainty propagation from build-up to wash-off confirmed that uncertainty in build-up process significantly influences wash-off process uncertainty. Specifically, the behaviour of particles <150µm during build-up primarily influences uncertainty propagation, resulting in appreciable variations in the pollutant load and composition during a wash-off event.

Intelligent reliability analysis with incomplete covariates

The reliability analysis is crucial to reducing unexpected down time, severe failures and ever tightened maintenance budget of engineering assets. Hazard based reliability methods are of particular interest as hazard reflects the current health status of engineering assets and their imminent failure risks. Most existing hazard models were constructed using the statistical methods. However, these methods were established largely based on two assumptions: one is the assumption of baseline failure distributions being accurate to the population concerned and the other is the assumption of effects of covariates on hazards. These two assumptions may be difficult to achieve and therefore compromise the effectiveness of hazard models in the application. To address this issue, a non-linear hazard modelling approach is developed in this research using neural networks (NNs), resulting in neural network hazard models (NNHMs), to deal with limitations due to the two assumptions for statistical models. With the success of failure prevention effort, less failure history becomes available for reliability analysis. Involving condition data or covariates is a natural solution to this challenge. A critical issue for involving covariates in reliability analysis is that complete and consistent covariate data are often unavailable in reality due to inconsistent measuring frequencies of multiple covariates, sensor failure, and sparse intrusive measurements. This problem has not been studied adequately in current reliability applications. This research thus investigates such incomplete covariates problem in reliability analysis. Typical approaches to handling incomplete covariates have been studied to investigate their performance and effects on the reliability analysis results. Since these existing approaches could underestimate the variance in regressions and introduce extra uncertainties to reliability analysis, the developed NNHMs are extended to include handling incomplete covariates as an integral part. The extended versions of NNHMs have been validated using simulated bearing data and real data from a liquefied natural gas pump. The results demonstrate the new approach outperforms the typical incomplete covariates handling approaches. Another problem in reliability analysis is that future covariates of engineering assets are generally unavailable. In existing practices for multi-step reliability analysis, historical covariates were used to estimate the future covariates. Covariates of engineering assets, however, are often subject to substantial fluctuation due to the influence of both engineering degradation and changes in environmental settings. The commonly used covariate extrapolation methods thus would not be suitable because of the error accumulation and uncertainty propagation. To overcome this difficulty, instead of directly extrapolating covariate values, projection of covariate states is conducted in this research. The estimated covariate states and unknown covariate values in future running steps of assets constitute an incomplete covariate set which is then analysed by the extended NNHMs. A new assessment function is also proposed to evaluate risks of underestimated and overestimated reliability analysis results. A case study using field data from a paper and pulp mill has been conducted and it demonstrates that this new multi-step reliability analysis procedure is able to generate more accurate analysis results.

Visualisation of Information Uncertainty : Progress and Challenges

Information uncertainty which is inherent in many real world applications brings more complexity to the visualisation problem. Despite the increasing number of research papers found in the literature, much more work is needed. The aims of this chapter are threefold: (1) to provide a comprehensive analysis of the requirements of visualisation of information uncertainty and their dimensions of complexity; (2) to review and assess current progress; and (3) to discuss remaining research challenges. We focus on four areas: information uncertainty modelling, visualisation techniques, management of information uncertainty modelling, propagation and visualisation, and the uptake of uncertainty visualisation in application domains.

Uncertainty quantification applied to the performance analysis of a conical diffuser

This paper offers an uncertainty quantification (UQ) study applied to the performance analysis of the ERCOFTAC conical diffuser. A deterministic CFD solver is coupled with a non-statistical generalised Polynomial Chaos(gPC)representation based on a pseudo-spectral projection method. Such approach has the advantage to not require any modification of the CFD code for the propagation of random disturbances in the aerodynamic field. The stochactic results highlihgt the importance of the inlet velocity uncertainties on the pressure recovery both alone and when coupled with a second uncertain variable. From a theoretical point of view, we investigate the possibility to build our gPC representation on arbitray grid, thus increasing the flexibility of the stochastic framework.

Predication and propagation : a method for analyzing evaluative meanings in technology policy

In this article I outline and demonstrate a synthesis of the methods developed by Lemke (1998) and Martin (2000) for analyzing evaluations in English. I demonstrate the synthesis using examples from a 1.3-million-word technology policy corpus drawn from institutions at the local, state, national, and supranational levels. Lemke's (1998) critical model is organized around the broad 'evaluative dimensions' that are deployed to evaluate propositions and proposals in English. Martin's (2000) model is organized with a more overtly systemic-functional orientation around the concept of 'encoded feeling'. In applying both these models at different times, whilst recognizing their individual usefulness and complementarity, I found specific limitations that led me to work towards a synthesis of the two approaches. I also argue for the need to consider genre, media, and institutional aspects more explicitly when claiming intertextual and heteroglossic relations as the basis for inferred evaluations. A basic assertion made in this article is that the perceived Desirability of a process, person, circumstance, or thing is identical to its 'value'. But the Desirability of anything is a socially and thus historically conditioned attribution that requires significant amounts of institutional inculcation of other 'types' of value-appropriateness, importance, beauty, power, and so on. I therefore propose a method informed by critical discourse analysis (CDA) that sees evaluation as happening on at least four interdependent levels of abstraction.

Asset specificity and behavioural uncertainty as moderators of the sales growth : employment growth relationship in emerging ventures

Sales growth and employment growth are the two most widely used growth indicators for new ventures; yet, sales growth and employment growth are not interchangeable measures of new venture growth. Rather, they are related, but somewhat independent constructs that respond differently to a variety of criteria. Most of the literature treats this as a methodological technicality. However, sales growth with or without accompanying employment growth has very different implications for managers and policy makers. A better understanding of what drives these different growth metrics has the potential to lead to better decision making. To improve that understanding we apply transaction cost economics reasoning to predict when sales growth will be or will not be accompanied by employment growth. Our results indicate that our predictions are borne out consistently in resource-constrained contexts but not in resource-munificent contexts.

Investment decision-making under risk (reliability) and uncertainty for infrastructure asset management

Risks and uncertainties are inevitable in engineering projects and infrastructure investments. Decisions about investment in infrastructure such as for maintenance, rehabilitation and construction works can pose risks, and may generate significant impacts on social, cultural, environmental and other related issues. This report presents the results of a literature review of current practice in identifying, quantifying and managing risks and predicting impacts as part of the planning and assessment process for infrastructure investment proposals. In assessing proposals for investment in infrastructure, it is necessary to consider social, cultural and environmental risks and impacts to the overall community, as well as financial risks to the investor. The report defines and explains the concept of risk and uncertainty, and describes the three main methodology approaches to the analysis of risk and uncertainty in investment planning for infrastructure, viz examining a range of scenarios or options, sensitivity analysis, and a statistical probability approach, listed here in order of increasing merit and complexity. Forecasts of costs, benefits and community impacts of infrastructure are recognised as central aspects of developing and assessing investment proposals. Increasingly complex modelling techniques are being used for investment evaluation. The literature review identified forecasting errors as the major cause of risk. The report contains a summary of the broad nature of decision-making tools used by governments and other organisations in Australia, New Zealand, Europe and North America, and shows their overall approach to risk assessment in assessing public infrastructure proposals. While there are established techniques to quantify financial and economic risks, quantification is far less developed for political, social and environmental risks and impacts. The report contains a summary of the broad nature of decision-making tools used by governments and other organisations in Australia, New Zealand, Europe and North America, and shows their overall approach to risk assessment in assessing public infrastructure proposals. While there are established techniques to quantify financial and economic risks, quantification is far less developed for political, social and environmental risks and impacts. For risks that cannot be readily quantified, assessment techniques commonly include classification or rating systems for likelihood and consequence. The report outlines the system used by the Australian Defence Organisation and in the Australian Standard on risk management. After each risk is identified and quantified or rated, consideration can be given to reducing the risk, and managing any remaining risk as part of the scope of the project. The literature review identified use of risk mapping techniques by a North American chemical company and by the Australian Defence Organisation. This literature review has enabled a risk assessment strategy to be developed, and will underpin an examination of the feasibility of developing a risk assessment capability using a probability approach.

Damage propagation in reinforced concrete frames under external blast loading

Iconic and significant buildings are the common target of bombings by terrorists causing large numbers of casualties and extensive property damage. Recent incidents were external bomb attacks on multi-storey buildings with reinforced concrete frames. Under a blast load circumstance, crucial damage initiates at low level storeys in a building and may then lead to a progressive collapse of whole or part of the structure. It is therefore important to identify the critical initial influence regions along the height, width and depth of the building exposed to blast effects and the structure response in order to assess the vulnerability of the structure to disproportionate and progressive collapse. This paper discusses the blast response and the propagation of its effects on a two dimensional reinforced concrete (RC) frame, designed to withstand normal gravity loads. The explicit finite element code, LS DYNA is used for the analysis. A complete RC portal frame seven storeys by six bays is modelled with reinforcement details and appropriate materials to simulate strain rate effects. Explosion loads derived from standard manuals are applied as idealized triangular pressures on the column faces of the numerical models. The analysis reports the influence of blast propagation as displacements and material yielding of the structural elements in the RC frame. The effected regions are identified and classified according to the load cases. This information can be used to determine the vulnerability of multi-storey RC buildings to various external explosion scenarios and designing buildings to resist blast loads.

Framework for investment decision-making under risk and uncertainty for infrastructure asset management

A study has been conducted to investigate current practices on decision-making under risk and uncertainty for infrastructure project investments. It was found that many European countries such as the UK, France, Germany including Australia use scenarios for the investigation of the effects of risk and uncertainty of project investments. Different alternative scenarios are mostly considered during the engineering economic cost-benefit analysis stage. For instance, the World Bank requires an analysis of risks in all project appraisals. Risk in economic evaluation needs to be addressed by calculating sensitivity of the rate of return for a number of events. Risks and uncertainties of project developments arise from various sources of errors including data, model and forecasting errors. It was found that the most influential factors affecting risk and uncertainty resulted from forecasting errors. Data errors and model errors have trivial effects. It was argued by many analysts that scenarios do not forecast what will happen but scenarios indicate only what can happen from given alternatives. It was suggested that the probability distributions of end-products of the project appraisal, such as cost-benefit ratios that take forecasting errors into account, are feasible decision tools for economic evaluation. Political, social, environmental as well as economic and other related risk issues have been addressed and included in decision-making frameworks, such as in a multi-criteria decisionmaking framework. But no suggestion has been made on how to incorporate risk into the investment decision-making process.

Uncertainty avoidance: public sector clients and procurement selection

Purpose: Choosing the appropriate procurement system for construction projects is a complex and challenging task for clients particularly when professional advice has not been sought. To assist with the decision making process, a range of procurement selection tools and techniques have been developed by both academic and industry bodies. Public sector clients in Western Australia (WA) remain uncertain about the pairing of procurement method to bespoke construction project and how this decision will ultimately impact upon project success. This paper examines ‘how and why’ a public sector agency selected particular procurement methods. · Methodology/Approach: An analysis of two focus group workshops (with 18 senior project and policy managers involved with procurement selection) is reported upon · Findings: The traditional lump sum (TLS) method is still the preferred procurement path even though alternative forms such as design and construct, public-private-partnerships could optimize the project outcome. Paradoxically, workshop participants agreed that alternative procurement forms should be considered, but an embedded culture of uncertainty avoidance invariably meant that TLS methods were selected. Senior managers felt that only a limited number of contractors have the resources and experience to deliver projects using the nontraditional methods considered. · Research limitations/implications: The research identifies a need to develop a framework that public sector clients can use to select an appropriate procurement method. A procurement framework should be able to guide the decision-maker rather than provide a prescriptive solution. Learning from previous experiences with regard to procurement selection will further provide public sector clients with knowledge about how to best deliver their projects.