Infrastructure Engineering - Theses

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    Influence of inertial and centrifugal forces on the flow rate and patterns of flow in fracture networks
    Malawara Arachchige, Heraji Hansika ( 2023-03)
    Inertial forces that resist the changes in velocity magnitude and direction are non-negligible even at low flow rates and cause non-trivial inertial pressure losses collapsing the linear relationship between flow rate and pressure drop in the fractured reservoirs. Though the effect of inertia on fluid flow dynamics in single fractures has been studied minutely, studies on both simple and intricate fracture networks hardly paid attention to the inertial effect. There is a huge lack of understanding of how inertia influences the flow through fracture networks and how it affects the flow partitioning between fracture sets. Moreover, inertial losses at the fracture intersection on the overall flow have not been addressed for real fracture outcrops. Therefore, the main objective of this research is, to investigate the fluid dynamics of inertia-dominated, non-Darcy flow in the fracture networks with paying attention to the flow rates that lead to regime transition and quantifying the effects of inertia at fracture networks on pressure distributions, patterns of flow and velocity distributions. As it is impossible to evaluate flow in intricate fracture networks that expands on metre scale via laboratory experiments, numerical simulations are applied to fracture outcrops. The first part of the dissertation deals with model verification for flow through simple fracture intersections where analytical solutions are available for laminar creeping flow. There, the numerical simulation results obtained from solving the full Navier-Stokes equation and results determined analytically based on the Darcy law are compared for model verification. Then the effect of intersection angle and aperture variations on flow in simple fracture intersections are analysed for a wider range of injection rates, starting from lower values, and then ramping up. The study based on the numerical simulations concluded that nonlinearity originates from the inertia effect enhanced by the intersection angle, and this becomes critical with the increment of flow rates. Though there is a cubic relationship between flow rate and pressure drop over the entire domain at low flow rates, it turns into a quadratic relationship with the increment of flow rate with distinct coefficients for laminar and turbulent regimes because of the inertial effect. After that, the study is extended to flow simulation in a natural fracture network mapped in the outcrop. The outcomes show that the inertial effect in real fracture outcrops becomes critical at very low flow compared to small fracture networks with a single intersection, causing flow regime transition. Moreover, it was identified that flow partitioning between sets and velocity spectra varies with the flow rate as flow patterns in fracture networks are highly dependent on the inertial effect. The overall outcomes of this research are expected to provide comprehensive knowledge of fluid flow dynamics in real fracture networks by considering the inertial effect. These findings are expected to be utilized by the researchers and the experts in the petroleum industry to enhance the well-performance, maintaining commercially viable rates. Moreover, these findings can be used in geothermal energy extraction, nuclear waste disposal, and groundwater management.
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    Towards improved irrigation scheduling through sensitivity analysis and remote sensing of crop coefficients
    Parehkar, Arash ( 2022)
    Agriculture faces significant challenges to increase food and fibre production under increasingly variable climate and uncertain supply of resources such as water. At present only 37% of the fresh water delivered to agricultural crops is used by crop in part due to inefficient management of irrigation. Therefore, optimal irrigation scheduling is important for saving water while maintaining crop productivity. To improve irrigation scheduling, it is important to identify the key input factors influencing the efficacy of the scheduling. In this study, the FAO56-based soil water balance irrigation scheduling method has been selected as one of the most widely-applied methods (Allen et al., 1998). Firstly, realistic ranges of the uncertainties of ten selected input factors of the method, including weather, soil, crop, and management factors, have been evaluated to assess their impact on irrigation scheduling. Then, the sensitivity of the irrigation scheduling to the uncertainty of each input factor is calculated using the Sobol’ global sensitivity analysis method. Results show that FAO56 crop coefficient, which has an expected average uncertainty of 20%, has the highest impact on irrigation scheduling. The sensitivity analysis was performed for eight different climates in Australia, which showed that the ranking of the influential factors on irrigation scheduling did not change with climate conditions, making crop coefficient the most sensitive factor in all climates. Since crop coefficient is an important factor in irrigation scheduling, FAO56 and remote-sensing-based Irrisat crop coefficients were compared with measured crop coefficients in one maize field in Australia and two lucerne fields in New Zealand. The Irrisat-derived dynamic crop coefficient values reproduced temporal changes in crop coefficient better than the FAO56 values. FAO56 crop coefficients were within a reasonable range for most periods of crop growth but they could not capture their temporal changes. However, Irrisat underestimated crop coefficients during the mid-season and end-season for maize, likely due to the saturation effect of the Normalised Difference Vegetation Index (NDVI) used to derive the dynamic crop coefficient. This problem was less significant in the lucerne fields since they feature lower biomass ranges. Overall, while FAO56 coefficients captured reasonable values in magnitude, the Irrisat-derived crop coefficient is superior in detecting temporal changes when it is available. Hence, using both of them in consideration of their advantages and disadvantages can help reduce the uncertainty in crop coefficients and ultimately save water in irrigation scheduling. Future research should focus on developing effective assimilation approaches to combine these different sources of crop coefficients, and thus ultimately improve the accuracy while reduce uncertainty of their estimates.
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    A Hybrid Framework for Short-term Irrigation Demand Forecasting
    Forouhar, Leila ( 2022)
    Reliable short-term estimates of irrigation water demand can provide valuable information to help water supply system operators with day-to-day operating decisions. Modeling irrigation demand is a complex task due to the different natural (soil, water, crop, and climate interactions) and behavioral (farmers' decision-making) components of the irrigation process. So far, various approaches have been attempted to estimate irrigation water needs in different contexts. Early studies have used simplified physical models to determine irrigation water needs conceptually. However, many of recent studies have applied data-driven methods to map the relationship between the principal influential factors and the water demand. In this study, a generic hybrid framework has been developed to forecast irrigation water demand by integrating a conceptual model (estimating crop water needs based on existing knowledge of the physical system) and a data-driven model (capturing the remaining input-output relationships that cannot be picked up by the conceptual model). The performance of this hybrid framework is evaluated based on real-world system data in Victoria, Australia, and compared to a benchmarking data-driven model (developed using a similar data-driven approach as the hybrid model). It was found that the proposed hybrid framework is able to estimate, with reasonable accuracy, daily irrigation water demand values up to 7 days ahead of the case study system. The hybrid model performs better than the data-driven benchmarking model for most lead times, and particularly for the high-demand period. The results demonstrate that integrating system understanding with data-driven modeling can lead to improved estimates of irrigation water demand. In addition to better predictive performance, the proposed hybrid framework provides improved system understanding and thus increased capacity to support operational decisions.
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    Low-fidelity Hydrodynamic Model-based Method for Efficient Flood Inundation Modelling
    Yang, Qi ( 2021)
    Flood is one of the most devastating natural hazards, as it often causes fatalities and damages to infrastructure. To develop strategies for flood risk mitigation, flood inundation models are often used to provide useful information for assessments of potential impacts of floods. Two-dimensional (2D) hydrodynamic models are commonly used for flood inundation simulations. However, they can be computationally intensive when used to simulate many flood events, for example for uncertainty analysis, or to simulate very large floodplains. To improve computational efficiency, data-driven models based on machine learning techniques and conceptual models based on simplified water-filling concepts have been developed. Data-driven models appear as black-box models and are yet to be used by many practitioners with confidence. Simplified conceptual models are generally not designed to simulate the temporal propagation of floods and are often only applied to estimate maximum/final flood extent and floodwater levels. In Australia, 2D hydrodynamic models have been established for many important catchments. There is potential to build on these existing models and develop methods to speed up flood inundation simulations. In this MPhil thesis, a new modelling method, LoHy+, is proposed based on existing 2D hydrodynamic models, to produce an efficient simulation of flood extent and depth with time. The method first develops a low-fidelity 2D hydrodynamic model (LFM) with coarse mesh based on an existing high-fidelity 2D hydrodynamic model (HFM). The aim of the LFM is to produce reasonably accurate simulation of water levels within the main river channels while tolerating poorer simulation elsewhere in the floodplain. Next, the method develops a Mapping Module by using training data to establish relationships between water levels in both river channels and across the floodplain generated using the HFM and water levels in the river channels generated using this LFM. In subsequent applications, the LFM is run first, and the Mapping Module is applied to estimate flood inundation within the entire model domain. The implementation of the LoHy+ is demonstrated using a real-world catchment located in the southern Murray Darling Basin, Australia. A fully calibrated HF MIKE21 FM hydrodynamic model is available for the catchment. The performance of the LoHy+ method is evaluated against simulation from the high-fidelity hydrodynamic model. There is a good agreement between results from the LoHy+ method and the original high-fidelity 2D hydrodynamic model. The new method is much more efficient and can simulate the spatiotemporal evolution of flood inundation with reasonable accuracy. It is potentially a useful tool for applications that require many model runs or long simulation durations.
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    Defect detection of concrete using ultrasonic pulse velocity and impact echo
    Song, Adam Yilun ( 2019)
    It has been known that concrete is one of the most important construction materials used in building industry. However, the internal defects, such as cracks and voids, can significantly affect the durability of concrete, ultimately the service-life of the structures. Therefore, the development of advanced non-destructive testing technologies becomes increasingly important. By performing experiments in conjunction with the development of theoretical models, the purpose of this study is to conduct concrete defect detection using Ultrasonic Pulse Velocity (UPV) and Impact Echo (IE) technologies. First, a series experimental studies were carried out by using concrete blocks with different defect characteristics (e.g. size and location). Then, both UPV and IE techniques were used for defect detection and the results were compared. Finally, FEM model was developed to further understand the fundamental mechanism of UPV in defect detection. It shows that the numerical simulation results can accurately capture the defect characteristics of concrete specimens. Through the comparison of the measurement results from UPV and IE, it demonstrates that UPV method could provide a more effective and reliable way for concrete internal defect detection through measuring the time taken by a pulse of ultrasonic wave passing through the samples. However, the frequency of the compression waves has to be carefully chosen in order to identify the defects in an efficient and accurate manner.
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    Novel approaches to investigate the complex interactions of bioturbation and clogging in the hyporheic zone
    Lakhanpal, Garima ( 2018)
    Bioturbation activity by macroinvertebrates in freshwaters may influence the hyporheic exchange processes in river systems. In this study, I focus on understanding the complex interaction of bioturbation by upward conveyor macroinvertebrates Lumbriculus variegatus and clogging processes in the hyporheic zone through novel methodological approaches. Upward conveyors are bottom feeders which align themselves vertically (heads buried in the sediment with their posterior ends at the surface water interface) and ingest and egest in a way that sediments move vertically upwards. The study aims at investigating (i) the effects of bioturbation on hyporheic flowpath patterns in clogged and unclogged sandy bedforms and (ii) the vertical spatial distribution of bioturbators in clogged sandy bedforms using an experimental setup. There is a paucity of agreed methodology to study the effects of bioturbation on hyporheic exchange processes in flowing waters. Therefore, this study is focused on developing the methods to understand these interactions in the laboratory flume. The above-mentioned objectives were addressed by running a series of dye tracing experiments in a recirculating perspex flume of dimensions 2.5m (L) X 0.2m (W) X 0.3m (H) filled with triple washed sand of 0.2mm mean particle size. The sand bed was later clogged with clay particles of 0.002mm to compare the flowpaths in clogged and unclogged bed conditions. Using image analysis of the injected dye flowpaths and X-ray technology for understanding the galleried structures created by the bioturbators we are able to better understand the role of these organisms in clogged river beds and their importance in the hyporheic exchange processes. Galleries were imaged using the micro-CT scans by taking core samples along the flume and scanning the top 5 cm of the core. This study can help us understand the need and importance to protect the bioturbator communities for maintaining ecosystem services and healthy functioning of the hyporheic zone.
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    Investigating the benefits of considering the payload spectra of freight vehicles on pavement costs based on weigh-in-motion data
    Ren, Jing ( 2017)
    Truck traffic is a crucial factor that contributes to pavement damage. The urbanization and globalization promote the higher level of daily consumption for goods, thus increasing the derived demand for freight transport. In some countries, such as Australia, there is a trend towards using larger vehicles, which raised the road authorities’ concern about their effect on pavement because of the lack of pavement maintenance and rehabilitation funding. Therefore, it is important to have a comprehensive understanding of Australian road freight market and optimize the allocation of freight for different types of trucks to reduce the total pavement damage. Weigh-in-motion (WIM) system, which measures and records detailed vehicle information operating on road, was the data source for this study. The data was provided by the State Road Authority of Victoria (VicRoads). This thesis gave out a prototype filtering strategy for WIM database to improve the accuracy. Also, it investigated the efficiency of freight transport by comparing the effect of six-axle semi-trailers and nine-axle B-doubles with regards to pavement performance when carrying various payloads. Mathematical models were developed to help decision makers consider how to distribute the road freight task more efficiently to minimize the pavement damage induced by freight vehicles. A simplified pavement performance prediction model was utilized as a basis to determine the future pavement maintenance & rehabilitation schedules and thus, help compare the long-term pavement treatment costs for different traffic loading scenarios. The outcomes of the research showed that it would have considerable advantages in reducing the overall pavement damage by decreasing the percentage of empty trucks, changing the proportion of freight carried by B-doubles as well as optimizing the payload distributions. In addition, there would be significant benefits in the pavement maintenance & rehabilitation costs over the pavement service life by improving the allocation of freight for trucks.
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    Answering queries for near places
    Wang, Hao ( 2017)
    Communication between people conveniently uses qualitative spatial terms, as shown by the high frequency of vague spatial prepositions such as ‘near’ in natural language corpora. The automatic interpretation of these terms, however, suffers from the challenges of capturing the conversational context in order to interpret such prepositions. This research presents an experimental approach to solicit impressions of near to identify distance measures that best approximate it (nuanced by the type of referent, and contrast sets). The presented model computes topological distances to sets of possible answers allowing a ranking of what is near in a context-aware manner. Context is introduced through contrast sets. The research compares the performance of topological distance, network distance, Euclidean distance, Manhattan distance, number of intersections, number of turns, and cumulative direction change. The aim of this comparison is to test whether a metric distance or topological distance is closer to human cognition, challenging the well-known paradigm of ’topology first, metric second’. The comparison results from our experiments show that topological distance appears to be closer to human perception of nearness than other distance measures only at larger scales, while a metric distance (Euclidean distance or Manhattan distance) is closer to how people perceive nearness in smaller scales. People with different sense-of-direction show no obvious differences in their inclination of the seven distance measures with regard to nearness. This research caters for the interpretation of ‘near’ with granular and local context, and provides a cognitively inspired method to answer near-queries automatically. The findings apply to urban environments and may need further verification in less structured environments.
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    Making the connection: a case study analysis of the public transport network effect in practice
    Lawrie, Iain James ( 2017)
    Public transit mode share in dispersed lower density cities common in North America and Australia is typically low. Except where users are captive, or where the density of activity is so high that congestion is a significant deterrent, transit generally fails to compete with the private car. However, a high proportion of private car use is associated with a range of economic, social and environmental challenges. A number of public transport operational approaches are available to policy makers. The ‘network effect’ is one operating approach specifically aimed at serving the complex travel patterns of dispersed lower density cities. The approach relies on effective transfers between transit modes. In compensation for the inconvenience of transfer, passengers are rewarded with a service providing access to a wide, rather than limited, geographic area. Whilst the theory is well established, analysis of its success in practice is less well researched. Network effect theory would benefit from further research into the relationship between urban density, service levels and rates of transfer between transit modes using real world case studies. Using the case study of the Dandenong railway line in Melbourne and the eastern branch of the Montreal Metro Green line, the thesis addressed three key questions. (i) Is there a level of feeder transit service above or below which we see substantial shifts in the number of people transferring?; (ii) which variables play the greatest role in influencing rates of transfer between transit modes?; and (iii) do subjective attributes of station urban design influence transfer numbers — everything else being broadly equal? Data was collected from transit operators, Australian Bureau of Statistics (ABS), Statistics Canada and by direct observations during field visits. An empirical quantitative approach was used to address question (i), a hierarchal linear multiple regression statistical approach used to address question (ii), whilst a quantitative approach was used for question (iii). It was found that where fewer than 100 feeder bus services operated per weekday day, limited transferring will occur, regardless of urban density. However, it was additionally found that frequent, connected services alone will not guarantee high rates of transfer. Evidence suggests direct bus routes, accessing stations at right angles to rail lines, are strongly correlated with higher rates of transfer, again regardless of density. The findings will be important for transit agencies looking to benefit from the theoretical advantages of the network effect. The results are particularly relevant to transport planners in Melbourne and Montreal where the Melbourne Metro and Reseau Electric Metropolitan (REM) projects will require substantial passenger access via feeder bus to achieve their patronage forecasts.
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    Identifying drivers of streamflow salinity in data-limited catchments
    Malana, Mohammad Naeem ( 2016)
    Salinisation of water and land is a serious problem affecting global environment, economy and food security. The strategies to mitigate salinity problem require an improved understanding of the factors causing it. This requires exploring the variability of streamflow salinity, which is an indicator of catchment salinity and highly variable in space and time. Previous research has focused on estimating streamflow salinity through a variety of models that are usually data-intensive, site specific and do not have wider applicability. The arid and semi-arid regions of the world, which are worst affected by the salinity problem, have data-limitations and commonly cannot support calibration of complex models. Therefore, a simple approach in analysis is required. This research project developed a simple approach for assessing the variability of streamflow salinity in data-limited catchments. This aim was achieved by developing a data-based approach to identify drivers of spatial and temporal variability of streamflow salinity. The spatial variability of streamflow salinity was analysed with the help of two methods. The first method used the Budyko curve to classify study catchments between the less-saline catchments and the catchments at risk of salinity. The Budyko curve analysis identified the long-term annual catchment water balance and climate as the main drivers of the spatial variability of streamflow salinity. The second method applied a statistical analysis and identified five drivers of streamflow salinity. These drivers are climate, topography, land-use, streamflow characteristics and mean annual evapotranspiration. The statistical model used long-term climate data and catchment static metrics. The catchment static metrics were derived from data on topography, climate, land-use and land-cover, geology, streamflow and salinity for the characterisation of study catchments. The 78 study catchments were projected into a physiographic space using the principal component analysis. This analysis was based on the catchment static metrics significantly associated with streamflow salinity. The statistical models involved ordinary least squares regression and inverse distance weighting interpolation method as the tools for prediction in the study region. The temporal variability in streamflow salinity was analysed at an annual and monthly time steps using a catchment water balance. It was demonstrated that catchment water balance is the driver of temporal variability in streamflow salinity based on the observed data. The Budyko curve and the Zhang et al. (2001) model were used to assess the inter-annual variability in the average streamflow salinity. A simple Thornthwaite-type monthly catchment water balance was used to explore the seasonal variations in streamflow salinity on time-series plots. However, no significant relationship between monthly water balance and streamflow salinity was detected on an ‘x-y’ scatter plot for all the study catchments, This is mainly due to uncertainty in data inputs, model error, the time-lag effect and bias in the observed streamflow salinity data. A simple data-driven approach was developed to analyse drivers of the spatial and temporal variability in streamflow salinity. This approach is suitable for data-limited catchments. The Budyko curve has the potential to explain both the spatial (geographical) as well as temporal (inter-annual) variability in the average annual streamflow salinity. The variability in streamflow salinity is derived by climate, catchment water balance and physical attributes of catchments. Most of these drivers are natural processes except for changes in land use. Therefore, the management of catchment water balance through vegetation management (e.g. reafforestation) is a practical measure to mitigate the salinity problem.