A new method for quantifying the contribution of different critical agents on railway bridge deterioration

Railway Bridges deteriorate over time due to different critical factors including, flood, wind, earthquake, collision, and environment factors, such as corrosion, wear, termite attack, etc. In current practice, the contributions of the critical factors, towards the deterioration of railway bridges, which show their criticalities, are not appropriately taken into account. In this paper, a new method for quantifying the criticality of these factors will be introduced. The available knowledge as well as risk analyses conducted in different Australian standards and developed for bridge-design will be adopted. The analytic hierarchy process (AHP) is utilized for prioritising the factors. The method is used for synthetic rating of railway bridges developed by the authors of this paper. Enhancing the reliability of predicting the vulnerability of railway bridges to the critical factors, will be the significant achievement of this research.

A novel method for quantifying the criticality and vulnerability of the components of railway bridges

Conditions of bridges deteriorate with age, due to different critical factors including, changes in loading, fatigue, environmental effects and natural events. In order to rate a network of bridges, based on their structural condition, the condition of the components of a bridge and their effects on behaviour of the bridge should be reliably estimated. In this paper, a new method for quantifying the criticality and vulnerability of the components of the railway bridges in a network will be introduced. The type of structural analyses for identifying the criticality of the components for carrying train loads will be determined. In addition to that, the analytical methods for identifying the vulnerability of the components to natural events whose probability of occurrence is important, such as, flood, wind, earthquake and collision will be determined. In order to maintain the practicality of this method to be applied to a network of thousands of railway bridges, the simplicity of structural analysis has been taken into account. Demand by capacity ratios of the components at both safety and serviceability condition states as well as weighting factors used in current bridge management systems (BMS) are taken into consideration. It will be explained what types of information related to the structural condition of a bridge is required to be obtained, recorded and analysed. The authors of this paper will use this method in a new rating system introduced previously. Enhancing accuracy and reliability of evaluating and predicting the vulnerability of railway bridges to environmental effects and natural events will be the significant achievement of this research.

Response of a buried tunnel to surface blast using different numerical techniques

This paper presents a comparative study on the response of a buried tunnel to surface blast using the arbitrary Lagrangian-Eulerian (ALE) and smooth particle hydrodynamics (SPH) techniques. Since explosive tests with real physical models are extremely risky and expensive, the results of a centrifuge test were used to validate the numerical techniques. The numerical study shows that the ALE predictions were faster and closer to the experimental results than those from the SPH simulations which over predicted the strains. The findings of this research demonstrate the superiority of the ALE modelling techniques for the present study. They also provide a comprehensive understanding of the preferred ALE modelling techniques which can be used to investigate the surface blast response of underground tunnels.

Statistical modelling of district-level residential electricity use in NSW, Australia

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of -1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the 'most' appropriate system when a review or upgrade of the network infrastructure is required.

Predicting freeway pavement construction cost using a back-propagation neural network : a case study in Henan, China

The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.

Navigating the future of roads – considering potential impacts of environmental and social trends on road infrastructure

Providing mobility corridors for communities, enabling freight networks to transport goods and services, and a pathway for emergency services and disaster relief operations, roads are a vital component of our societal system. In the coming decades, a number of modern issues will face road agencies as a result of climate change, resource scarcity and energy related challenges that will have implications for society. To date, these issues have been discussed on a case by case basis, leading to a fragmented approach by state and federal agencies in considering the future of roads – with potentially significant cost and risk implications. Within this context, this paper summarises part of a research project undertaken within the ‘Greening the Built Environment’ program of the Sustainable Built Environment National Research Centre (SBEnrc, Australia), which identified key factors or ‘trends’ affecting the future of roads and key strategies to ensure that road agencies can continue to deliver road infrastructure that meets societal needs in an environmentally appropriate manner. The research was conducted over two years, including a review of academic and state agency literature, four stakeholder workshops in Western Australia and Queensland, and industry consultation. The project was supported financially and through peer review and contribution, by Main Roads Western Australia, QLD Department of Transport and Main Roads, Parsons Brinckerhoff, John Holland Group, and the Australian Green Infrastructure Council (AGIC). The project highlighted several potential trends that are expected to affect road agencies in the future, including predicted resource and materials shortages, increases in energy and natural resources prices, increased costs related to greenhouse gas emissions, changing use and expectations of roads, and changes in the frequency and intensity of weather events. Exploring the implications of these potential futures, the study then developed a number of strategies in order to prepare transport agencies for the associated risks that such trends may present. An unintended outcome of the project was the development of a process for enquiring into future scenarios, which will be explored further in Stage 2 of the project (2013-2014). The study concluded that regardless of the type and scale of response by the agency, strategies must be holistic in approach, and remain dynamic and flexible.

Integrated approach for diagnostics and prognostics of HP LNG pump based on health state probability estimation

Effective machine fault prognostic technologies can lead to elimination of unscheduled downtime and increase machine useful life and consequently lead to reduction of maintenance costs as well as prevention of human casualties in real engineering asset management. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique and historical failure knowledge embedded in the closed loop diagnostic and prognostic system. To estimate a discrete machine degradation state which can represent the complex nature of machine degradation effectively, the proposed prognostic model employed a classification algorithm which can use a number of damage sensitive features compared to conventional time series analysis techniques for accurate long-term prediction. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for the comparison of intelligent diagnostic test using five different classification algorithms. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state probability using the Support Vector Machine (SVM) classifier. The results obtained were very encouraging and showed that the proposed prognostics system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

Synthetic rating procedures for railway bridges

One of the most important parts of any Bridge Management System (BMS) is the condition assessment and rating of bridges. This paper, introduces a procedure for condition assessment, based on criticality and vulnerability analysis. According to this procedure, new rating equations are developed. The inventory data is used to determine the contribution of different critical factors such as environmental effects, flood, earthquake, wind, and vehicle impacts. The criticality of the components to live load and vulnerability of the components to the above critical factors are identified. Based on the criticality and the vulnerability of the components and criticality of factors, and by using the new rating equations, the condition assessment and the rating of the railway bridges and their components at the network level will be conducted. This method for the first time incorporates structural analysis, available knowledge of risk assessment in structural engineering standards, and the experience of structural engineers in a practical way to enhance the reliability of the condition assessment and rating a network of bridges.

Environmental indicators for sustainable road development and operation

Road construction, maintenance and operation are activities that impact the environment by way of energy use, resource consumption and emission. Components such as construction material, transportation, street lighting, rolling resistance, traffic congestion during works, albedo and end-of-life processing impact the environment at different phases of the life of a road. With a view to promote sustainable development, a few sustainability rating schemes, e.g. Infrastructure Sustainability and Invest (Australia), Envision and Greenroads (USA), and CEEQUAL (UK) have been developed, that can assess road projects. These schemes address environmental areas such as: energy and emission, land, water, materials, discharges into surroundings, waste and ecology as factors for sustainable development. This paper assesses different rating schemes based on a defined comprehensive life cycle assessment (LCA) system boundary for road projects to identify different environmental indicators that address sustainable road development and operation. The findings indicate that new indicators are required to address different environmental components during the operation phase of roads.

Impact and energy absorption of portable water-filled road safety barrier system fitted with foam

Portable water-filled barriers (PWFBs) are roadside appurtenances that are used to prevent errant vehicles from penetrating into temporary construction zones on roadways. A numerical model of the composite PWFB, consisting of a plastic shell, steel frame, water and foam was developed and validated against results from full scale experimental tests. This model can be extended to larger scale impact cases, specifically ones that include actual vehicle models. The cost-benefit of having a validated numerical model is significant and this allows the road barrier designer to conduct extensive tests via numerical simulations prior to standard impact tests Effects of foam cladding as additional energy absorption material in the PWFB was investigated. Different types of foam were treated and it was found that XPS foam was the most suitable foam type. Results from this study will aid PWFB designers in developing new generation of roadside structures which will provide enhanced road safety.

Using the Alliance Form for Operation and Maintenance of Privatized Infrastructures

A significant number of privatizations utilized to operate and maintain critical networked infrastructures have failed to meet contractual expectations and the expectations of the community. The author carried out empirical research ex-ploring four urban water systems. This research revealed that of the four forms of privatization the alliance form was particularly suited to the stewardship of an ur-ban water system. The question then is whether these findings from urban water can be generalised to O&M of infrastructure generally. The answer is increasingly important as governments seek financial sustainability through reapplying the contestability strategy and outsource and privatise further services and activities. This paper first examines the issues encountered with O & M privatisations. Second the findings as to the stewardship achieved by the four case study water systems are unpacked with particular focus upon the alliance form. Third the key variables which were found to have distinct causal links to the stewardship-like behaviour of the private participants in the Alliance case study are described. Fourth the variables which may be crucial to the successful application of the alliance form to the broader range of infrastructures are separated out. Fifth this paper then sets the path for research into these crucial features of the alliance form.

Integrating the impact of climate into event based failure models for water pipes

This paper presents an event-based failure model to predict the number of failures that occur in water distribution assets. Often, such models have been based on analysis of historical failure data combined with pipe characteristics and environmental conditions. In this paper weather data have been added to the model to take into account the commonly observed seasonal variation of the failure rate. The theoretical basis of existing logistic regression models is briefly described in this paper, along with the refinements made to the model for inclusion of seasonal variation of weather. The performance of these refinements is tested using data from two Australian water authorities.

Seasonal factors influencing the failure of buried water reticulation pipes

While the use of environmental factors in the analysis and prediction of failures of buried reticulation pipes in cold environments has been the focus of extensive work, the same cannot be said for failures occurring on pipes in other (non-freezing) environments. A novel analysis of pipe failures in such an environment is the subject of this paper. An exploratory statistical analysis was undertaken, identifying a peak in failure rates during mid to late summer. This peak was found to correspond to a peak in the rate of circumferential failures, whilst the rate of longitudinal failures remained constant. Investigation into the effect of climate on failure rates revealed that the peak in failure rates occurs due to differential soil movement as the result of shrinkage in expansive soils.

Sewer performance reporting : factors that influence blockages

Managing sewer blockages represents a significant operational challenge for water utilities. In Australia, company-level blockage rates are used to compare the effectiveness of the management strategies of different utilities. Anecdotal evidence suggests this may not be a fair basis for comparison because blockages are influenced by a range of factors beyond management control and that vary from company to company. This issue was investigated as part of a broader research effort on sewer blockage management undertaken in conjunction with the Water Services Association of Australia (WSAA) and its members. A Web-based survey was used to collate expert opinion on factors that influence blockage rate. The identified factors were then investigated in an exploratory analysis of blockage-related data provided by two participating utilities, supported by literature reviews. The results indicate that blockage rate is influenced by a range of factors, including asset attributes, climatic conditions, water consumption, and soil type. Since these factors vary from utility to utility, this research supports the assertion that company-level blockage rate is not in itself an appropriate metric for comparing management effectiveness.

Understanding how the Australian climate can affect pipe failure

This paper details a statistical analysis of historical failure data, which focuses on determining the manner in which local climate affects pipe failure rates. It was found that seasonality exists in the data, indicating an affect of local climate on failure rate. Significant variation in failure rates was seen between the months of December and May, especially in February/March, whilst limited variations were seen in other months of the year. Further analysis found that failure rates were strongly correlated with minimum antecedent precipitation index and net evaporation and that climate affected failure rate by influencing soil moisture content. Interaction affects between static attributes of the pipe-environment system and local climate were also investigated.