Report : review of the literature : maintenance and rehabilitation costs for roads (Risk-based Analysis)

Realistic estimates of short- and long-term (strategic) budgets for maintenance and rehabilitation of road assessment management should consider the stochastic characteristics of asset conditions of the road networks so that the overall variability of road asset data conditions is taken into account. The probability theory has been used for assessing life-cycle costs for bridge infrastructures by Kong and Frangopol (2003), Zayed et.al. (2002), Kong and Frangopol (2003), Liu and Frangopol (2004), Noortwijk and Frangopol (2004), Novick (1993). Salem 2003 cited the importance of the collection and analysis of existing data on total costs for all life-cycle phases of existing infrastructure, including bridges, road etc., and the use of realistic methods for calculating the probable useful life of these infrastructures (Salem et. al. 2003). Zayed et. al. (2002) reported conflicting results in life-cycle cost analysis using deterministic and stochastic methods. Frangopol et. al. 2001 suggested that additional research was required to develop better life-cycle models and tools to quantify risks, and benefits associated with infrastructures. It is evident from the review of the literature that there is very limited information on the methodology that uses the stochastic characteristics of asset condition data for assessing budgets/costs for road maintenance and rehabilitation (Abaza 2002, Salem et. al. 2003, Zhao, et. al. 2004). Due to this limited information in the research literature, this report will describe and summarise the methodologies presented by each publication and also suggest a methodology for the current research project funded under the Cooperative Research Centre for Construction Innovation CRC CI project no 2003-029-C.

Identification of critical input variables for risk-based cost estimates for road maintenance and rehabilitation

An estimation of costs for maintenance and rehabilitation is subject to variation due to the uncertainties of input parameters. This paper presents the results of an analysis to identify input parameters that affect the prediction of variation in road deterioration. Road data obtained from 1688 km of a national highway located in the tropical northeast of Queensland in Australia were used in the analysis. Data were analysed using a probability-based method, the Monte Carlo simulation technique and HDM-4’s roughness prediction model. The results of the analysis indicated that among the input parameters the variability of pavement strength, rut depth, annual equivalent axle load and initial roughness affected the variability of the predicted roughness. The second part of the paper presents an analysis to assess the variation in cost estimates due to the variability of the overall identified critical input parameters.

A risk-based approach to course quality assurance

A decade ago, Queensland University of Technology (QUT) developed an innovative annual Courses Performance Report, but through incremental change, this report became quite labour-intensive. A new risk-based approach to course quality assurance, that consolidates voluminous data in a simple dashboard, responds to the changing context of the higher education sector. This paper will briefly describe QUT’s context and outline the second phase of implementation of this new approach to course quality assurance. The main components are: Individual Course Reports (ICRs), the Consolidated Courses Performance Report (CCPR), Underperforming Courses Status Update and the Strategic Faculty Courses Update (SFCU). These components together form a parsimonious and strategic annual cycle of reporting and place QUT in a positive position to respond to future sector change

Risk-based approach to the management of on-site wastewater treatment facilities

Background On-site wastewater treatment system (OWTS) siting, design and management has traditionally been based on site specific conditions with little regard to the surrounding environment or the cumulative effect of other systems in the environment. The general approach has been to apply the same framework of standards and regulations to all sites equally, regardless of the sensitivity, or lack thereof, to the receiving environment. Consequently, this has led to the continuing poor performance and failure of on-site systems, resulting in environmental and public health consequences. As a result, there is increasing realisation that more scientifically robust evaluations in regard to site assessment and the underlying ground conditions are needed. Risk-based approaches to on-site system siting, design and management are considered the most appropriate means of improvement to the current standards and codes for on-site wastewater treatment systems. The Project Research in relation to this project was undertaken within the Gold Coast City Council region, the major focus being the semi-urban, rural residential and hinterland areas of the city that are not serviced by centralised treatment systems. The Gold Coast has over 15,000 on-site systems in use, with approximately 66% being common septic tank-subsurface dispersal systems. A recent study evaluating the performance of these systems within the Gold Coast area showed approximately 90% were not meeting the specified guidelines for effluent treatment and dispersal. The main focus of this research was to incorporate strong scientific knowledge into an integrated risk assessment process to allow suitable management practices to be set in place to mitigate the inherent risks. To achieve this, research was undertaken focusing on three main aspects involved with the performance and management of OWTS. Firstly, an investigation into the suitability of soil for providing appropriate effluent renovation was conducted. This involved detailed soil investigations, laboratory analysis and the use of multivariate statistical methods for analysing soil information. The outcomes of these investigations were developed into a framework for assessing soil suitability for effluent renovation. This formed the basis for the assessment of OWTS siting and design risks employed in the developed risk framework. Secondly, an assessment of the environmental and public health risks was performed specifically related the release of contaminants from OWTS. This involved detailed groundwater and surface water sampling and analysis to assess the current and potential risks of contamination throughout the Gold Coast region. Additionally, the assessment of public health risk incorporated the use of bacterial source tracking methods to identify the different sources of fecal contamination within monitored regions. Antibiotic resistance pattern analysis was utilised to determine the extent of human faecal contamination, with the outcomes utilised for providing a more indicative public health assessment. Finally, the outcomes of both the soil suitability assessment and ground and surface water monitoring was utilised for the development of the integrated risk framework. The research outcomes achieved through this project enabled the primary research aims and objects to be accomplished. This in turn would enable Gold Coast City Council to provide more appropriate assessment and management guidelines based on robust scientific knowledge which will ultimately ensure that the potential environmental and public health impacts resulting from on-site wastewater treatment is minimised. As part of the implementation of suitable management strategies, a critical point monitoring program (CPM) was formulated. This entailed the identification of the key critical parameters that contribute to the characterised risks at monitored locations within the study area. The CPM will allow more direct procedures to be implemented, targeting the specific hazards at sensitive areas throughout Gold Coast region.

Condition-based maintenance optimisation without a predetermined strategy structure for a two-component series system

Most existing research on maintenance optimisation for multi-component systems only considers the lifetime distribution of the components. When the condition-based maintenance (CBM) strategy is adopted for multi-component systems, the strategy structure becomes complex due to the large number of component states and their combinations. Consequently, some predetermined maintenance strategy structures are often assumed before the maintenance optimisation of a multi-component system in a CBM context. Developing these predetermined strategy structure needs expert experience and the optimality of these strategies is often not proofed. This paper proposed a maintenance optimisation method that does not require any predetermined strategy structure for a two-component series system. The proposed method is developed based on the semi-Markov decision process (SMDP). A simulation study shows that the proposed method can identify the optimal maintenance strategy adaptively for different maintenance costs and parameters of degradation processes. The optimal maintenance strategy structure is also investigated in the simulation study, which provides reference for further research in maintenance optimisation of multi-component systems.

A test rig for the condition-based maintenance application on the traction chain of very high speed trains

The preventive maintenance of traction equipment for Very High Speed Trains (VHST) nowadays is becoming very expensive owing to the high complexity and quality of these components that require high reliability. An efficient maintenance approach like the Condition-Based Maintenance (CBM) should be implemented to reduce the costs. For this purpose, an experimental full-scale test rig for the CBM of VHST traction equipment has been designed to investigate in detail failures in the main mechanical components of system, i.e. motor, bearings and gearbox. The paper describes the main characteristics of this unique test rig, able to reproduce accurately the train operating conditions, including the relative movements of the motor, the gearbox and the wheel axle. Gearbox, bearing seats and motor are equipped by accelerometers, thermocouples, torque meter and other sensors in different positions. The testing results give important information about the most suitable sensor position and type to be installed for each component and show the effectiveness of the techniques used for the signal analysis in order to identify faults of the gearbox and motor bearings.

Development of health risk-based metrics for defining a heatwave : a time series study in Brisbane, Australia

Background: This study attempted to develop health risk-based metrics for defining a heatwave in Brisbane, Australia. Methods: Poisson generalised additive model was performed to assess the impact of heatwaves on mortality and emergency hospital admissions (EHAs) in Brisbane. Results: In general, the higher the intensity and the longer the duration of a heatwave, the greater the health impacts. There was no apparent difference in EHAs risk during different periods of a warm season. However, there was a greater risk of mortality in the second half of a warm season than that in the first half. While elderly (>75 years)were particularly vulnerable to both the EHA and mortality effects of a heatwave, the risk for EHAs also significantly increased for two other age groups (0-64 years and 65-74 years) during severe heatwaves. Different patterns between cardiorespiratory mortality and EHAs were observed. Based on these findings, we propose the use of a teiered heat warning system based on the health risk of heatwave. Conclusions: Health risk-based metrics are a useful tool for the development of local heatwave definitions. thsi tool may have significant implications for the assessment of heatwave-related health consequences and development of heatwave response plans and implementation strategies.

Optimizing preventative maintenance strategies for linear assets

Linear assets are engineering infrastructure, such as pipelines, railway lines, and electricity cables, which span long distances and can be divided into different segments. Optimal management of such assets is critical for asset owners as they normally involve significant capital investment. Currently, Time Based Preventive Maintenance (TBPM) strategies are commonly used in industry to improve the reliability of such assets, as they are easy to implement compared with reliability or risk-based preventive maintenance strategies. Linear assets are normally of large scale and thus their preventive maintenance is costly. Their owners and maintainers are always seeking to optimize their TBPM outcomes in terms of minimizing total expected costs over a long term involving multiple maintenance cycles. These costs include repair costs, preventive maintenance costs, and production losses. A TBPM strategy defines when Preventive Maintenance (PM) starts, how frequently the PM is conducted and which segments of a linear asset are operated on in each PM action. A number of factors such as required minimal mission time, customer satisfaction, human resources, and acceptable risk levels need to be considered when planning such a strategy. However, in current practice, TBPM decisions are often made based on decision makers’ expertise or industrial historical practice, and lack a systematic analysis of the effects of these factors. To address this issue, here we investigate the characteristics of TBPM of linear assets, and develop an effective multiple criteria decision making approach for determining an optimal TBPM strategy. We develop a recursive optimization equation which makes it possible to evaluate the effect of different maintenance options for linear assets, such as the best partitioning of the asset into segments and the maintenance cost per segment.

Risk-assessment practices in the private rental sector : implications for low-income renters (Final Report)

This report focuses on risk-assessment practices in the private rental market, with particular consideration of their impact on low-income renters. It is based on the fieldwork undertaken in the second stage of the research process that followed completion of the Positioning Paper. The key research question this study addressed was: What are the various factors included in ‘risk-assessments’ by real estate agents in allocating ‘affordable’ tenancies? How are these risks quantified and managed? What are the key outcomes of their decision-making? The study builds on previous research demonstrating that a relatively large proportion of low-cost private rental accommodation is occupied by moderate- to high-income households (Wulff and Yates 2001; Seelig 2001; Yates et al. 2004). This is occurring in an environment where the private rental sector is now the de facto main provider of rental housing for lower-income households across Australia (Seelig et al. 2005) and where a number of factors are implicated in patterns of ‘income–rent mismatching’. These include ongoing shifts in public housing assistance; issues concerning eligibility for rent assistance; ‘supply’ factors, such as loss of low-cost rental stock through upgrading and/or transfer to owner-occupied housing; patterns of supply and demand driven largely by middle- to high-income owner-investors and renters; and patterns of housing need among low-income households for whom affordable housing is not appropriate. In formulating a way of approaching the analysis of ‘risk-assessment’ in rental housing management, this study has applied three sociological perspectives on risk: Beck’s (1992) formulation of risk society as entailing processes of ‘individualisation’; a socio-cultural perspective which emphasises the situated nature of perceptions of risk; and a perspective which has drawn attention to different modes of institutional governance of subjects, as ‘carriers of specific indicators of risk’. The private rental market was viewed as a social institution, and the research strategy was informed by ‘institutional ethnography’ as a method of enquiry. The study was based on interviews with property managers, real estate industry representatives, tenant advocates and community housing providers. The primary focus of inquiry was on ‘the moment of allocation’. Six local areas across metropolitan and regional Queensland, New South Wales, and South Australia were selected as case study localities. In terms of the main findings, it is evident that access to private rental housing is not just a matter of ‘supply and demand’. It is also about assessment of risk among applicants. Risk – perceived or actual – is thus a critical factor in deciding who gets housed, and how. Risk and its assessment matter in the context of housing provision and in the development of policy responses. The outcomes from this study also highlight a number of salient points: 1.There are two principal forms of risk associated with property management: financial risk and risk of litigation. 2. Certain tenant characteristics and/or circumstances – ability to pay and ability to care for the rented property – are the main factors focused on in assessing risk among applicants for rental housing. Signals of either ‘(in)ability to pay’ and/or ‘(in)ability to care for the property’ are almost always interpreted as markers of high levels of risk. 3. The processing of tenancy applications entails a complex and variable mix of formal and informal strategies of risk-assessment and allocation where sorting (out), ranking, discriminating and handing over characterise the process. 4. In the eyes of property managers, ‘suitable’ tenants can be conceptualised as those who are resourceful, reputable, competent, strategic and presentable. 5. Property managers clearly articulated concern about risks entailed in a number of characteristics or situations. Being on a low income was the principal and overarching factor which agents considered. Others included: - unemployment - ‘big’ families; sole parent families - domestic violence - marital breakdown - shift from home ownership to private rental - Aboriginality and specific ethnicities - physical incapacity - aspects of ‘presentation’. The financial vulnerability of applicants in these groups can be invoked, alongside expressed concerns about compromised capacities to manage income and/or ‘care for’ the property, as legitimate grounds for rejection or a lower ranking. 6. At the level of face-to-face interaction between the property manager and applicants, more intuitive assessments of risk based upon past experience or ‘gut feelings’ come into play. These judgements are interwoven with more systematic procedures of tenant selection. The findings suggest that considerable ‘risk’ is associated with low-income status, either directly or insofar as it is associated with other forms of perceived risk, and that such risks are likely to impede access to the professionally managed private rental market. Detailed analysis suggests that opportunities for access to housing by low-income householders also arise where, for example: - the ‘local experience’ of an agency and/or property manager works in favour of particular applicants - applicants can demonstrate available social support and financial guarantors - an applicant’s preference or need for longer-term rental is seen to provide a level of financial security for the landlord - applicants are prepared to agree to specific, more stringent conditions for inspection of properties and review of contracts - the particular circumstances and motivations of landlords lead them to consider a wider range of applicants - In particular circumstances, property managers are prepared to give special consideration to applicants who appear worthy, albeit ‘risky’. The strategic actions of demonstrating and documenting on the part of vulnerable (low-income) tenant applicants can improve their chances of being perceived as resourceful, capable and ‘savvy’. Such actions are significant because they help to persuade property managers not only that the applicant may have sufficient resources (personal and material) but that they accept that the onus is on themselves to show they are reputable, and that they have valued ‘competencies’ and understand ‘how the system works’. The parameters of the market do shape the processes of risk-assessment and, ultimately, the strategic relation of power between property manager and the tenant applicant. Low vacancy rates and limited supply of lower-cost rental stock, in all areas, mean that there are many more tenant applicants than available properties, creating a highly competitive environment for applicants. The fundamental problem of supply is an aspect of the market that severely limits the chances of access to appropriate and affordable housing for low-income rental housing applicants. There is recognition of the impact of this problem of supply. The study indicates three main directions for future focus in policy and program development: providing appropriate supports to tenants to access and sustain private rental housing, addressing issues of discrimination and privacy arising in the processes of selecting suitable tenants, and addressing problems of supply.

Condition-based prognosis of machine health

Modern machines are complex and often required to operate long hours to achieve production targets. The ability to detect symptoms of failure, hence, forecasting the remaining useful life of the machine is vital to prevent catastrophic failures. This is essential to reducing maintenance cost, operation downtime and safety hazard. Recent advances in condition monitoring technologies have given rise to a number of prognosis models that attempt to forecast machinery health based on either condition data or reliability data. In practice, failure condition trending data are seldom kept by industries and data that ended with a suspension are sometimes treated as failure data. This paper presents a novel approach of incorporating historical failure data and suspended condition trending data in the prognostic model. The proposed model consists of a FFNN whose training targets are asset survival probabilities estimated using a variation of Kaplan-Meier estimator and degradation-based failure PDF estimator. The output survival probabilities collectively form an estimated survival curve. The viability of the model was tested using a set of industry vibration data.

Can book-to-market and size be risk factors that predict economic growth in Asia's emerging economies?

We investigate whether the two 2 zero cost portfolios, SMB and HML, have the ability to predict economic growth for markets investigated in this paper. Our findings show that there are only a limited number of cases when the coefficients are positive and significance is achieved in an even more limited number of cases. Our results are in stark contrast to Liew and Vassalou (2000) who find coefficients to be generally positive and of a similar magnitude. We go a step further and also employ the methodology of Lakonishok, Shleifer and Vishny (1994) and once again fail to support the risk-based hypothesis of Liew and Vassalou (2000). In sum, we argue that search for a robust economic explanation for firm size and book-to-market equity effects needs sustained effort as these two zero cost portfolios do not represent economically relevant risk.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.