Infrastructure Engineering - Research Publications
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ItemChanges in joint lubrication with the degree of meniscectomy and osteochondral junction integrity(Elsevier BV, 2023-11)This study focuses on the relationship between meniscectomy and osteochondral junction health, and their integrity on cartilage lubrication. Using a previously published multi-component joint computational model, we explored the impact of increasing degree of meniscectomy and osteochondral flow conductivity on joint lubrication. Results suggest a greater effect of meniscectomy on joint lubrication when the osteochondral junction is healthy. However, the impact is less pronounced when the osteochondral junction is already diseased due to compromised lubrication capability. This research provides a first-time quantitative analysis of this interaction, which highlights the importance of adequately evaluating the osteochondral junction’s condition before meniscectomy surgery. It also suggests that reducing post-surgery activity level may be beneficial for patients with diseased junctions undergoing meniscectomy.
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ItemUsing Ensemble Streamflow Forecasts to Inform Seasonal Outlooks for Water Allocations in the Murray Darling Basin(ASCE-AMER SOC CIVIL ENGINEERS, 2023-09-01)Water is a limited and highly valuable resource. In many parts of the world, water agencies allocate water according to agreed entitlement systems. The allocations are largely based on water already available in storages and rivers. Water agencies may also issue seasonal water allocation outlooks by anticipating future inflows to the storages and rivers. These outlooks are meant to assist water entitlement holders to plan for their crop planting, irrigation, and participation in water markets. Currently, these outlooks are generally based on historical inflow observations (climatology) and are often determined for a small selection of possible climatic scenarios (e.g., extreme dry, dry, average, and wet). These outlooks have large uncertainties, which require users to manage high risks themselves, leading to inefficient water use. In this study, we investigate the use of ensemble seasonal inflow forecasts to improve the production of seasonal water allocation outlooks through a case study of the Goulburn system in central Victoria, Australia. This is a complex system with active water trade both within the region and outside with the larger connected southern Murray-Darling Basin. In this case study, we integrate Australian Bureau of Meteorology's seasonal streamflow forecasts with Goulburn-Murray Water's water allocation to produce fully probabilistic water allocation outlooks. We evaluate the outlooks for three irrigation seasons from 2017 to 2020. We compare these outlooks with those produced from using inflows based on climatology only, an approach akin to the current practice of Goulburn-Murray Water. Using seasonal streamflow forecasts resulted in outlooks up to 60% (average 20%) closer to actual determinations, with uncertainty reduced by up to 65% (average 19%) Improvements were most obvious for short lead times and later in the irrigation season. This is a clear demonstration of how integration of streamflow forecasts can improve end-user products, which can lead to more efficient water use and water market participation.
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ItemChanges in flood-associated rainfall losses under climate change(ELSEVIER, 2023-10)Climate change is expected to impact the severity and frequency of floods, which has implications for flood risk management. Design floods and derived flood frequency curves obtained using event-based rainfall-runoff models are widely used in industry to assess flood risks for planning and design purposes. For these approaches it is necessary to have (a) rainfall inputs, and (b) rainfall losses specified, the latter representing the amount of rainfall that is either intercepted, stored on the surface, or infiltrated into the soil and does not contribute to the flood hydrograph. There is extensive research on changes in flooding under climate change that focus on projections of rainfall. However, there is little research into projections of rainfall losses under climate change, despite the knowledge that their changes will modulate the flood response. Here, we present one of the first studies seeking to quantify how rainfall losses, as represented by estimates of initial and continuing losses used in event-based models, are projected to change under climate change. We identify dependencies between rainfall losses and antecedent soil moisture in around half (over 200) of the largely unregulated catchments (i.e. watersheds) in Australia analysed in this study and use these relationships to project rainfall losses under climate change. Near universal increases in both the mean and variance of both initial losses and continuing losses are projected in these catchments, suggesting that increased rainfall losses could offset the impact of increased rainfalls for frequently occurring floods and result in an increased variance in flood responses.
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ItemHigh water use plants influence green roof substrate temperatures and their insulative benefits(Elsevier BV, 2023-12-01)Green roofs are amongst the solutions employed to deliver sustainable buildings in cities. Their vegetation and substrate layers can reduce the heat transfer through the roof, thus potentially reducing energy used for building cooling and heating. However, little research has investigated the insulative properties of drought-tolerant plants which also have high water use. These plants have been found to improve runoff retention by removing larger volumes of water from the substrate through higher transpiration rates than succulents. This planting strategy may also enhance green roof cooling performance due to their greater evapotranspiration rates. In this study, the thermal performance of three drought-tolerant species with high water use — Lomandra longifolia, Dianella admixta, and Stypandra glauca — was evaluated and compared with a commonly used succulent species (Sedum pachyphyllum) and a bare unplanted module. L. longifolia had the best insulative performance during the entire investigated period, reducing green roof substrate surface temperature up to 1.86 °C compared to succulent S. pachyphyllum. In summer, the mixture reduced heat gain to a greater extent than monoculture plantings of all species except L. longifolia. Summer measurements also suggest that plants with high leaf area index (LAI) and higher albedo should be selected to reduce surface temperatures. High evapotranspiration rates of high water use L. longifolia led to greatest reduction of bottom surface temperatures during a heatwave when decreasing its water content from 18.5% to 2.9%. Results obtained using an analytical hierarchical partitioning technique indicated air temperature had the most significant impact on temperatures at both the surface of the planting substrate and the bottom of each green roof unit, accounting for 48% to 58% of the variation.
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ItemNo Preview AvailableManaging underground legal boundaries in 3D - Extending the CityGML standard(Elsevier BV, 2023-08)Legal boundaries are used for delineating the spatial extent of ownership property’s spaces. In underground environments, these boundaries are defined by referencing physical objects, surveying measurements, or projections. However, there is a gap in connecting and managing these boundaries and underground legal spaces, due to a lack of data model. A 3D data model supporting underground land administration (ULA) should define and model these boundaries and the relationships between them and underground ownership spaces. Prominent 3D data models can be enriched to model underground legal boundaries. This research aims to propose a new taxonomy of underground legal boundaries and model them by extending CityGML, which is a widely used 3D data model in the geospatial science domain. We developed, implemented, and tested the model for different types of underground legal boundaries. The implemented prototype showcased the potential benefits of CityGML for managing underground legal boundaries in 3D. The proposed 3D underground model can be used to address current challenges associated with communicating and managing legal boundaries in underground environments. While this data model was specifically developed for Victoria, Australia, the proposed model and approach can be used and replicated in other jurisdictions by adjusting the data requirements for underground legal boundaries.
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ItemLand use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia.(Nature Portfolio, 2023-08-19)Accurate spatial information on Land use and land cover (LULC) plays a crucial role in city planning. A widely used method of obtaining accurate LULC maps is a classification of the categories, which is one of the challenging problems. Attempts have been made considering spectral (Sp), statistical (St), and index-based (Ind) features in developing LULC maps for city planning. However, no work has been reported to automate LULC performance modeling for their robustness with machine learning (ML) algorithms. In this paper, we design seven schemes and automate the LULC performance modeling with six ML algorithms-Random Forest, Support Vector Machine with Linear kernel, Support Vector Machine with Radial basis function kernel, Artificial Neural Network, Naïve Bayes, and Generalised Linear Model for the city of Melbourne, Australia on Sentinel-2A images. Experimental results show that the Random Forest outperforms remaining ML algorithms in the classification accuracy (0.99) on all schemes. The robustness and statistical analysis of the ML algorithms (for example, Random Forest imparts over 0.99 F1-score for all five categories and p value [Formula: see text] 0.05 from Wilcoxon ranked test over accuracy measures) against varying training splits demonstrate the effectiveness of the proposed schemes. Thus, providing a robust measure of LULC maps in city planning.
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ItemModelling underground cadastral survey data in CityGML(Wiley, 2023)In underground environments, survey elements such as survey points and observations provide the information required to define legal boundaries. These elements are also used to connect underground legal spaces to a geodetic survey network. Due to the issues of current 2D approaches for managing underground cadastral data, prominent 3D data models have been extended to support underground land administration. However, previous studies mostly focused on defining underground legal spaces and boundaries, with less emphasis on survey elements. This research aims to extend CityGML to support underground cadastral survey data. The proposed extension is based on the survey elements elicited from underground cadastral plans, which is then implemented for an underground case study area in Melbourne, Australia. This extension integrates underground survey data with legal and physical data in a 3D digital environment and provides an improved representation of survey elements, facilitating the management and communication of underground cadastral survey data.
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ItemThe carbon footprint of treating patients with septic shock in the intensive care unit(College of Intensive Care Medicine of Australia and New Zealand, 2018-12-01)OBJECTIVE: To use life cycle assessment to determine the environmental footprint of the care of patients with septic shock in the intensive care unit (ICU). DESIGN, SETTING AND PARTICIPANTS: Prospective, observational life cycle assessment examining the use of energy for heating, ventilation and air conditioning; lighting; machines; and all consumables and waste associated with treating ten patients with septic shock in the ICU at BarnesJewish Hospital, St. Louis, MO, United States (US-ICU) and ten patients at Footscray Hospital, Melbourne, Vic, Australia (Aus-ICU). MAIN OUTCOME MEASURES: Environmental footprint, particularly greenhouse gas emissions. RESULTS: Energy use per patient averaged 272 kWh/day for the US-ICU and 143 kWh/day for the Aus-ICU. The average daily amount of single-use materials per patient was 3.4 kg (range, 1.0-6.3 kg) for the US-ICU and 3.4 kg (range, 1.2-8.7 kg) for the Aus-ICU. The average daily particularly greenhouse gas emissions arising from treating patients in the US-ICU was 178 kg carbon dioxide equivalent (CO2-e) emissions (range, 165-228 kg CO2-e), while for the Aus-ICU the carbon footprint was 88 kg CO2-e (range, 77-107 kg CO2-e). Energy accounted for 155 kg CO2-e in the US-ICU (87%) and 67 kg CO2-e in the Aus-ICU (76%). The daily treatment of one patient with septic shock in the US-ICU was equivalent to the total daily carbon footprint of 3.5 Americans' CO2-e emissions, and for the Aus-ICU, it was equivalent to the emissions of 1.5 Australians. CONCLUSION: The carbon footprints of the ICUs were dominated by the energy use for heating, ventilation and air conditioning; consumables were relatively less important, with limited effect of intensity of patient care. There is large opportunity for reducing the ICUs' carbon footprint by improving the energy efficiency of buildings and increasing the use of renewable energy sources.
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ItemTransferable supervised learning model for public transport service load estimation(Springer Science and Business Media LLC, 2023-07-28)Detailed knowledge of service utilisation and passenger load profiles is the basis for the design, operation, and adjustment of a public transport service. The advancement in sensing technologies enable transit operators to monitor the variabilities in passenger flows continuously and consistently. There is a growing body of literature on using supervised learning models with direct passenger counts from historical observations. However, the incomplete, inaccurate, and biased data from automatic sensors pose challenges in this process. This paper proposes novel supervised learning models to estimate the onboard load profile of public transport services based on two main data sources: (1) limited data collected on a subset of service vehicles by automatic passenger counting (APC) systems, and (2) fare data collected by automatic fare collection (AFC) systems. The specific consideration is given to the fact that the developed models can be transferred across different routes. This is motivated by the commonly “limited coverage” of automated passenger counter devices on service vehicles. We introduce an array of new models, including a superior segment-based model, which demonstrates remarkable improvement in model transferability and accuracy. The proposed methodology utilises separate methods in different segments of a transit line. The proposed models were applied to three tram lines in Melbourne, Australia, where various types of shortcomings exist in the automated data. The test results demonstrate that the proposed models can be transferred and applied to other transit route without relying on historical observations. This would enable transit operators to reduce the number of required devices and monitor service utilisation in a more cost-efficiently manner, particularly in public transport networks where AFC coverage is usually incomplete and negatively skewed. The information on service utilisation will not only help operators to accommodate the variability in passenger demand but also assist passengers in journey planning to avoid overcrowding on services.
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ItemModelling driver's response to demand management strategies for electric vehicle charging in Australia(Elsevier, 2023-09)Electric vehicles (EVs) can help decarbonise transport as long as the energy sector successfully balances the electricity supply with the demand and maximises the use of renewable resources. For that, demand-side management strategies are necessary to encourage users to charge at certain times through time-of-use (ToU) tariffs or to control the load provided for charging (i.e., supplier-managed charging). However, adapting to different charging times can disrupt users’ schedule flexibility. This study investigates consumer preferences for smart charging technology and control (user-managed or supplier-managed) and responses to progressive ToU tariff discounts for guiding changes in EV charging time in Australia. We analyse the potential of ToU tariffs in shifting demand to late-night hours and around midday when there is a peak in solar energy generation. Based on a sample of 994 drivers (including 97 EV owners), we estimated a multinomial choice model to identify key predictors of individual preference for smart charging management and a bivariate ordered model to investigate consumer response to time-of-use discounts. The results show that activity-travel behaviour is an important predictor of both demand-side management strategies. Consumers willing to change EV charging time in response to ToU tariffs are likely to have more flexible schedules, while those with more time constraints seek the practical benefits of smart charging. Current EV owners have higher propensities than potential adopters to choose supplier-managed smart charging and charge around midday in response to ToU tariffs. This indicates that trials with current EV owners may overestimate acceptance of these strategies. Our findings show that while monetary incentives can successfully shift an important share of consumers to night-time hours, these incentives are not very effective in shifting to midday charging. Synergy in formulating transport and energy demand strategies may be necessary to facilitate such a shift.