Infrastructure Engineering - Theses

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 451
  • Item
    Thumbnail Image
    Effect of flow regulation and artificial watering on phytoplankton dynamics in an arid floodplain lakes system
    Wijesuriya, Muthukuda Wijesuriya Arachchilage Sewwandi Uttara Kumari ( 2022-12)
    Wetlands are amongst the most productive, diverse and dynamic ecosystems on Earth. They are highly dependent on lateral connectivity with the river, but ever-increasing water resource development has interrupted the river flow mainly through water infrastructure, excessive water extraction and flow regulation. This has severely degraded floodplain ecosystems by limiting the amount of water flowing into the wetlands. As a result, restoration of wetlands has become a focal point worldwide. The field of environmental flows has been developed as an effective and reliable restoration method for these floodplains as well as their connected rivers. An environmental flow is an amount of water legally set aside to be used for environmental purposes. The aim of environmental flows is not only to restore the wetlands, but also to improve the biodiversity and habitat diversity to benefit both the environment and dependent human livelihoods. Although environmental flows have a long history, a water-efficient, engineering-based approach to environmental flows has been widely practiced during the past few decades. For wetlands, the main objective of this approach is to restore the ecosystem without having to provide overbank floods. This artificial watering of wetlands is achieved through the use of water pumps to inundate the floodplain and then by the use of channels, regulators and weirs to control and retain the water within the floodplain. Environmental flows are provided to a specific wetland to achieve a predetermined set of ecological objectives. These mainly include the improvement of macrophytes, fish, frogs, turtles, waterfowl and macroinvertebrate communities. However, while all levels of the trophic cascade are important to achieve positive outcomes, environmental water planning usually ignores the importance of the phytoplankton that are the base of the food web. A consequence of this lack of attention on phytoplankton is that floodplain lakes are often affected by algal blooms after environmental water events. Such blooms result in high ecological and aesthetic damage as well as economic losses through disrupting recreational activities. The main objective of this research was to investigate the factors that regulate phytoplankton dynamics after environmental watering in an arid floodplain system, taking the Hattah Lakes floodplain as a case study system. The research was aimed at providing suggestions to incorporate the “phytoplankton component” into its current flow management. Hattah Lakes is located in the Hattah-Kulkyne national park, in North-West Victoria, Australia. It contains a scattered distribution of floodplain lakes, from which 12 are part of a Ramsar site. From over 16 floodplain lakes, six, easily accessible lakes were selected to carry out this research. The main research objective was investigated through three research questions. The first research question investigated the spatiotemporal variation of sediment-sourced recruitment of phytoplankton and the role of the seedbank in shaping the pelagic phytoplankton populations. Experiments revealed that the germination of phytoplankton resting stages is asynchronous between and within lakes, which are potentially regulated by strain- and species-specific traits. There was spatial heterogeneity of phytoplankton resting stages both between and within lakes. This suggests that local environmental cues or local drivers are important in driving phytoplankton development and succession, even in the face of community homogenization during the initial flooding. Among these driving factors, water depth and rate of water level increase appeared to be important. The results demonstrate the potential advantages of incorporating a practice called time-share flooding into the current flow regime. In time-share flooding, water is traded back and forth between adjacent water bodies. This can exhaust sediment-sourced recruitment of phytoplankton and thereby minimize the risk of future algal blooms. The second research question addressed the influence of blackwater on phytoplankton. Blackwater is Dissolved Organic Carbon (DOC) rich water which is created upon floodplain inundation. A laboratory culture of the most common bloom forming cyanobacterial species, Dolichospermum circinalis, was used to test the effect of blackwater from different plant sources and intensities on population development. The same types of blackwater were also used to assess the effects of blackwater on phytoplankton resting stage germination. The results showed that blackwater has a temporary control effect on the cyanobacterial population and resting stage germination of multiple species. The effect of blackwater was dependent on the source, but blackwater derived from leaf litter had the greatest effect in terms of phytoplankton control. However, since increasing the availability of leaf litter on the floodplain is not consistent with environmental watering objectives, this result and the impermanence of its effect demonstrated that blackwater cannot be used as a phytoplankton control method despite the impact it has on phytoplankton dynamics. The third research question was aimed at identifying patterns observed in phytoplankton populations throughout the wet-dry cycle of a floodplain lake. We used Lake Kramen within the Hattah Lakes complex as the case study site for this work. Water samples for phytoplankton and physical and chemical analysis were collected monthly over a period of 18 months, from the early stages of filling of a dry lakebed, through its peak volumes and onto when it was almost dry again. The lake gradually transformed from a green-algae dominated system into a cyanobacteria-dominated system, and the hydrological stability (or large-scale water level fluctuations) of the lake played a vital role in this process. The results further emphasized the importance of time-share flooding to disrupt the hydrological stability for bloom prevention, as well as to achieve the environmental water objective of frequent floods. Laboratory results from the earlier chapters were used to explain the population dynamics observed in the field-based observations of Lake Kramen and the results of three research chapters collectively show that the current environmental flow regime have a direct impact upon phytoplankton population dynamics. Failure to address this issue may have immediate or delayed negative impacts on the ecological goals of environmental water. Results suggest that time-share flooding is the most reliable, effective, practical, and water-efficient management practice that could address the issue of excessive phytoplankton growth in the Hattah Lakes system. This management intervention could be used in other wetland systems with engineered environmental flows, where the flow can be regulated within the floodplain system. The outputs of this research can potentially benefit water managers, policy makers, and researchers to improve the current flow regimes to achieve the maximum benefit from invested environmental water.
  • Item
    Thumbnail Image
    Streamflow prediction using water level data in ungauged basins
    Jian, Jie ( 2022-12)
    Rainfall-runoff modelling is widely used to understand and quantify hydrological processes and is applied in water resource management, streamflow predictions and decision-making processes. Traditional calibration methods require accurate and continuous discharge time series. However, the majority of rivers in the world are ungauged or sparsely gauged, and continuous discharge data are hard to access in these locations. Thus new calibration methods that do not rely on discharge time series are worth investigating. Water level data could be a good replacement to discharge since there is a roughly monotonic relationship between discharge and water level in most nature rivers. Also, water level data are more achievable than discharge and the satellite altimeters could provide global coverage of water level monitoring data along their overpass. As a result, to use water level data directly in rainfall-runoff modelling is a promising approach for streamflow prediction in ungauged basins. This thesis develops two calibration approaches to implement water level data into rainfall-runoff modelling. They are (1) Spearman Rank correlation (SRC) based approach, which calibrates modelled streamflow against observed water level using Spearman Rank correlation. (2) Inverse Rating Curve (IRC) function-based approach, which introduces three more parameters to simulate water level from an inversed rating curve. The new approaches are tested to be promising with high correlations between observed and estimated discharge. However, the results contain large biases between observations and estimated discharge data due to the lack of discharge information in the calibration process. To mitigate the biases, some observed discharge values are imported into the calibration processes to help anchor the results. It is proved that the calibration performances can be improved by incorporating a small number of discharge values, and among them the high-flow data (such as the 95th percentile of the discharge data) is the most important one. However, it is hard to obtain high flow values in real life, especially in ungauged catchments. To improve the feasibility of the new calibration approaches, regionalised flow data are reproduced using easily-accessed hydroclimatic data and catchment characteristics. With the help of the regionalised flow information, the calibration performances of the new approaches improved a lot, and the modelling performances are comparable to that when observed discharge indices were used. In addition, better results could be seen in wetter catchments. Finally, the methods are extended to real ungauged catchments where ground measured water level data are absent. Altimetry data are applied in this case and the modelling performances are comparable to the calibrations with gauged water-level data if accurate altimetry data could be obtained. Also, the tolerance of the new methods to the measurement frequency and observation errors are tested, since altimetry data have lower temporal frequencies and higher observation errors compared to gauged water-level data. Results show that the altimeter-based calibration performance is not significantly influenced by the low observation frequency up to 15 days, but it deteriorates more sensitively with observation errors. Overall, the new water-level based calibration method developed in this thesis provides a promising approach for future rainfall-runoff modelling in ungauged or data-sparse areas.
  • Item
    Thumbnail Image
    Site-Specific Ground Motions for Dynamic Analyses in Regions of Lower Seismicity
    HU, Yiwei ( 2022)
    Site-specific dynamic analyses of structures have many advantages over traditional code spectrum procedures in regions of lower seismicity. The prime reason is that the site-specific response spectra and accelerograms are more realistic representations of earthquake actions for a structure located on a unique construction site. Developing site-specific ground motions requires a comprehensive understanding of regional seismic hazard analyses, soil condition analyses and site response analyses. Guidelines or facilities for performing site-specific dynamic analyses in accordance with the design code are unavailable to engineering practitioners in Australia. The primary objective of this thesis is to develop a computationally effective method to generate response spectra and accelerograms for site-specific dynamic analysis in intraplate regions of lower seismicity, with a focus on the Southeastern Australia (SEA) region. Based on the proposed method, this thesis provides suites of ground motions in compliance with the Australian standard for direct engineering applications. The conditional mean spectrum (CMS) methodology was first reviewed and its challenges for application in intraplate regions were overcome by employing a diversity of ground motion prediction expressions (GMPEs) and the uniform seismicity model. Three different schemes using a weighted averaging of candidate GMPEs were adopted, and a comparison of predictions demonstrated only minor differences confirming the robustness of the modelling. The constructed CMS were targeted for sourcing ground motions to define seismic hazard at the bedrock level. The bedrock motions were amplified through soil column models to simulate site amplification effects. Subsoil information retrieved from multiple borehole records from the same site was sampled to construct soil column models to achieve conservative estimations of soil amplification ratio at the fundamental period of vibration of the structure to account for resonance. The sampling process involves closed-form expressions for determining the shear strain profile in a soil column considering degradation in the shear modulus of the soil in seismic conditions. The applications of resultant site-specific response spectra and accelerograms, following a ground motion selection scheme proposed by the author, were demonstrated with nonlinear time history analysis for structural design and multiple stripe analysis for risk assessment. This thesis is concluded with three outcomes: (1) a ground motion database for site-specific seismic design based on twenty sites that typify subsoil profiles in SEA, (2) an online program at https://quakeadvice.org/ for generating ground motions with user-defined borehole information, and (3) suites of ground motions for risk assessment of structures following the multiple stripe analysis method.
  • Item
    Thumbnail Image
    Toward Constant Service Quality Monitoring in Transport Nodes
    Rahimi, Mohammad Masoud ( 2022)
    Promoting public transport relies on developing effective tools for the proactive management of service quality in public transport nodes. However, the challenging environment of these nodes, characterised by crowding and congestion, limited physical space, and frequent changes, poses obstacles to existing monitoring approaches. This calls for novel data-driven methods to provide insight into the management of such a challenging environment. This study fills this gap by combining subjective and objective service quality measures, along with proposing state-of-the-art models, to enable constant assessment of service quality in the confined spaces of transport nodes. The resulting insights are expected to support prompt, context-aware, and flexible decision-making, benefiting local communities, transport authorities, and related industries. First, the feasibility of integrating unstructured data sources, such as social media content, for concurrent monitoring of service quality in transport nodes is investigated. To that end, tweets are mined to evaluate various service quality characteristics. To realize this, a novel framework based on a fine-tuned language model and sentiment analysis is proposed. This allows for the classification of tweets and the effective detection of unusual events impacting perceived service quality. Findings highlight the constraints of sparse yet valuable data, where extra knowledge from sentiment analysis improves the monitoring tool's sensitivity in a variety of settings. Next, a novel framework is designed to discover and predict events impacting service quality across a network of constrained public transport nodes. To that end, social media content offers a unique opportunity to effectively model such a complex phenomenon. Nonetheless, this is challenging. Apart from data sparsity, the asynchronous nature of the observations impedes pattern discovery and event prediction. To tackle this, Hawkes Point Process is used to model events without making them discrete. Moreover, sentiment analysis is used to strengthen the model with extra information. Results demonstrate the effectiveness of the approach in identifying the causal patterns and predicting events over the limited context. Then, Closed-Circuit Television (CCTV) footage as an authoritative dataset is used to monitor pedestrians' behaviour on platforms and objectively measure service quality in transport nodes. A pre-requisite is to accurately identify and localize pedestrians in the footage. However, this is challenging as pedestrians are often partially occluded in the crowded environment of transport nodes. To address this, a novel human pose-aware pedestrian localization framework is proposed to ensure a fast and accurate location of pedestrians. Results show the effectiveness of the approach in pedestrian localization. Finally, a novel framework for an effective combination of subjective and objective service quality measures in transport nodes is provided. To this end, multivariate Hidden Markov Models and vision-based Level of Service estimation are used to ensure robustness in the fusion of both datasets with significantly different characteristics. The model is strengthened by employing extracted sentiment information from social media feeds as additional knowledge about passengers' perceptions. Results show the efficacy of the framework compared to data-intensive state-of-the-art data fusion approaches. Overall, this study develops a range of new methods and brings new theories and technologies together, to enable concurrent monitoring of public transport infrastructure performance. The results of this study can be used as a contributing module in Cyber-Physical systems to constantly monitor passenger-infrastructure interactions. The findings would not only have a positive impact on people's quality of life but also inspire researchers from other disciplines, especially the public transport community, to employ data-driven solutions in a variety of interesting ways.
  • Item
    No Preview Available
    Multi-Scale Life Cycle Energy Assessment of Australian Residential Buildings
    Li, Shengping ( 2022)
    The Australian Government has set targets to reduce greenhouse gas (GHG) emissions from energy use, such as achieving net-zero emissions by 2050. The residential building sector is material-intensive and responsible for substantial amounts of energy use and GHG emissions. Thus, decarbonising the residential building sector plays a significant role in climate change mitigation in Australia. To reduce energy use in residential buildings, it is necessary to conduct a comprehensive analysis of energy in residential buildings across the life cycle stages and different scales of the built environment, as well as provide details about the impact of various strategies on life cycle energy (LCE) in residential buildings. Therefore, this research assesses the material stocks (MSs) and LCE of Australian residential buildings from the bottom-up perspective across multiple scales (i.e., material, component, building, and regional scales). In addition, this research analyses future LCE trajectories in residential buildings under different scenarios and provides strategies to help the Australian residential building sector achieve the 2050 net-zero emissions target. This study has been applied to case studies of Australian residential buildings in the Inner Melbourne Cities and the state of Victoria. The research results present a comprehensive view of MSs and LCE of Australian residential buildings across multiple scales. At the building scale, this research provides a validated reference for material and energy intensities of various Australian residential building typologies. The results also indicate that the operational energy (OE) of new residential buildings has significantly decreased with the widespread implementation of energy-efficient strategies. As a result, the proportion of embodied energy (EE) in LCE has increased from 9%-35% for residential buildings constructed before 2011 to 66%-71% for those built after 2011. At the regional scale, materials (i.e., concrete and brick) and components (i.e., foundation, slab, and external wall) are the main contributors to residential building stocks, which can be considered as the main sources for recycling. The energy embodied in concrete contributes the largest part of total EE. Heating, appliances, and hot water contribute the most significant proportion of OE in residential building stocks. Moreover, this research demonstrates the spatial distribution and temporal dynamics of MSs, EE and embodied GHG emissions of residential building stocks. Furthermore, this research analyses future LCE trajectories in residential buildings with different energy-saving strategies in four scenarios: high carbon (HC), business-as-usual (BAU), accelerated policy (AP), and net-zero emissions (NZE). The research results reveal the potential upward trajectory of OE in the HC scenario, but a downward trend in the NZE scenario from 2020 to 2050. The most effective strategies for decarbonising the residential building sector include improving the building envelope efficiency, upgrading heating and hot water systems, and using renewable energy. In addition, EE will contribute more to LCE after 2045 in AP and NZE scenarios with the decrease of OE. The EE in the NZE scenario is higher than that in the other three scenarios, as energy-efficient buildings usually use more insulation materials. Therefore, EE reduction is critical for achieving net-zero life cycle emissions in the residential building sector. It is recommended that building stakeholders promote demand-side, supply-side, and EE reduction strategies synergistically to achieve net-zero emissions from a life cycle perspective. The comprehensive profile of residential buildings across multiple scales supports decision-makers in identifying key contributors to LCE and implementing effective measures for decarbonising the built environment. The detailed spatiotemporal results provide new insights for evidence-based decision-making on material management and energy conservation towards a more circular building sector. An explicit analysis of future LCE trajectories with different strategies facilitates informed decision-making towards the 2050 net-zero emissions target.
  • Item
    Thumbnail Image
    Life-cycle structural performance assessment for road structures
    Chen, Shilun ( 2022)
    Road structures, including bridges, tunnels, slopes, and road networks, stimulate economic and social development by providing access for the movement of people, goods and services, and have become a significant asset for all nations worldwide. With the extended operating life of existing road structures, structural vulnerabilities increase accordingly and are subject to material degradation, fatigue and defects. It is vital for road authorities to conduct effective management and maintenance of those assets and this requires the application of engineering technology, data analysis and sound financial practices. There is an ongoing and urgent need for road authorities to accurately examine the remaining service life of operating road structures. The typical process of asset management includes data collection and analysis. In this field, two of the most widely used methods of data analysis are model based and data driven. The modelling-based method uses collected structural and non-structural parameters to create a simulating platform to mimic the behavior of the entire system. Since the modelling-based method is applied on a case-by-case basis, different models should be established for the corresponding types of structures. The data-driven method is performed based on previously monitored data to fit the trend of similarity and to predict the state of structural systems, including reliability analysis methods, artificial intelligence techniques and other statistical approaches. Many research studies have been conducted to develop either model-based or data-driven methods for the assessment of the condition of road structures. However, how to combine these two methods and more effectively foresee structural lifelong conditions based on real-world inspection data from engineering practices, remains a critical challenge. The key information on structural health, including defects and damage, is embedded in these data generated via various detection techniques. Therefore, this thesis aims to propose customized, innovative frameworks for the life-cycle assessments of the condition of road structures, using both model-based and data-driven methods, based on data obtained from structural integrity inspections and structural dynamic behavior monitoring. In this thesis, the research objective is achieved with 5 main studies. Study 1 systematically investigates the impact of concrete crack-induced reinforcement corrosion on the serviceability of concrete bridges by developing a reliability-based engineering approach involving an autoregressive crack propagation model and a steel corrosion prediction model. The model parameters were calibrated using the eight-year inspection data of an operating bridge. The results predict that, although the surface crack of an RC bridge is repairable through periodic maintenance, the corrosion of the steel bars in the bridge continues over time with a corrosion rate that depends on the different maintenance intervention cycle periods. For instance, reducing the maintenance intervention cycle periods from 12 years to 4 years could potentially prolong the service life of the bridge by around 15 years. Study 2 presents an innovative framework for assessment of the condition of health of stay cables based on cable vibration frequencies from interferometric radar (IBIS-FS) using engineering reliability analysis (ERA). The results show that the presented framework can remotely monitor the accurate real-time load bearing condition of stay cables by calculating tension force and effectively assessing their condition of health. In study 3, an engineering reliability (ERA)-based framework for assessing the life-cycle condition of tunnel lining is developed to provide crack maintenance suggestions for road authorities. Using the conditional random field theory (RFT), the crack width growth rate and crack propagation in tunnel lining can be forecast based on historical crack data. Then, the reliability index of the individual lining segments and the overall tunnel lining can be determined using the first-order second-moment (FOSM) method and system reliability analysis. The developed framework is implemented for a highway tunnel, which shows that the proposed framework can generate a life-cycle assessment for the tunnel lining based on the prediction of crack propagation and can potentially assist decision-makers with conducting lining crack maintenance and sealing. Study 4 introduces a developed framework of system reliability-based assessment for a road network under a high slope using a fuzzy neural network (FNN). FNN as a technique for processing a large dataset can be utilized to automatically assess each slope risk level. Then, the reliability of the overall road network under a high slope is assessed based on the slope risk level and the corresponding slope reliability using system reliability analysis. The proposed framework is also applied to a real highway network. The results show that a fuzzy neural network (FNN) can effectively and efficiently assess the slope risk level with a 0.9009 correlation coefficient and 0.2511 root mean squared error. In addition, the reliability index of the overall highway network increases by 35% after slope maintenance, which provides a slope management and maintenance guide for highway authorities. In study 5, a system reliability-based model for developing optimized maintenance strategies of bridges with the aim of improving the overall reliability of a bridge dominated road network is proposed. The results show that the developed model can quantify the impact of the different maintenance strategies of a bridge on the overall road network, and thereby could potentially assist decision-makers to design effective and efficient maintenance strategies for prolonging the service life of the bridge dominated road networks. The findings in this thesis indicate that, by combining material degradation and structural defect-related dynamic behavior, the developed frameworks are capable of generating a comprehensive assessment of structural health and forecasting structural performance in the long term. The presented life-cycle structural performance assessment can provide effective and efficient structural management and maintenance guides for road authorities.
  • Item
    No Preview Available
    Wave variability of Bass Strait and south-east Australia
    Liu, Jin ( 2022)
    South-east Australia, is exposed to the Southern Ocean and is one of the most densely populated regions of Australia. Understanding wave variability for this region is crucial to the adaptation of coastal ecosystems, installation and operation of nearshore and offshore facilities, beach morphology, etc. However, there are few studies which have been undertaken to investigate the wave variability of this region. The thesis aims to fill the knowledge gap and advance our understanding in this area by developing three high-resolution third-generation wave models (WAVEWATCH III, WW3) based on unstructured grids. Before undertaking the detailed wave analysis for the region, the thesis investigated the impacts of climate variability on ocean waves over the period 1981-2020 from a global perspective. Six commonly used climate indices were considered to describe large-scale oscillations. These indexes include El Nino-Southern Oscillation (ENSO), Antarctic Oscillation (AAO) or Southern Annular Mode (SAM), Arctic Oscillation (AO) or Northern Annular Mode (NAM), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO) and Indian Ocean Dipole (IOD). Linear regression of wave parameters against climate indices and composite analysis of wave parameters over significant event years during December-January-February (DJF) and June-July-August (JJA) show the strength of ocean wave responses over different oceanic regions. Based on an empirical orthogonal function (EOF) analysis of significant wave height (Hs) anomalies, it is shown that interannual variability of Hs anomalies in DJF and JJA is associated with different climate drivers. Wavelet analysis of principle components of Hs anomalies and climate indices provides further details of their evolution and co-variability over the time-frequency domain. Having a general understanding of the modulation of climate variability on global ocean waves, the thesis focuses on the wave variability of south-east Australia. The thesis develops a regional wave hindcast model of Bass Strait and south-east Australia over the period 1981-2020, which is driven by ERA5 reanalysis winds. The simulation results are extensively validated against the Victorian coastal buoy network and multiple satellite altimeter observations, which show good agreement. The model outputs are further used to study the wave climate and wave power climate across the domain, which emphasizes the impacts of Southern Ocean swell and protection provided by the landmass of Tasmania. A total of 14 nearshore locations along the south-east Australian coast are selected to conduct detailed wave power characterization. The expected electricity power potential is estimated based on 9 typical wave energy converters (WECs). These results provide a benchmark for coastal WEC deployment in the future. Future wave conditions of Bass Strait and south-east Australia by the end of the twenty-first century (2071-2100) under two greenhouse gas emission scenarios (SSP1-2.6 and SSP5-8.5) were investigated based on two regional wave climate models (RWCMs), which are driven by the ACCESS-CM2 and EC-Earth3 CMIP6 global circulation models (GCMs). Both RWCMs indicate that the projected Hs(SSP5-8.5) changes are primarily influenced by Southern Ocean swell in spring and winter, whilst wind-sea is dominant in summer and autumn. However, different projected spatial distributions of Hs are found in the coastal regions, which are affected by local synoptic weather events and GCM winds. It also shows that the long-term wave climate variability in the domain is impacted by the SAM. These changes may have potential implications for Tasmanian and Victorian coastline stability.
  • Item
    Thumbnail Image
    Development of efficient flood inundation modelling schemes using deep learning
    Zhou, Yuerong ( 2022)
    Flood inundation models are one of the important tools used to manage flood-related risks in engineering practices such as infrastructure design, flooding disaster mitigation, and reservoir operations. Two-dimensional (2D) hydrodynamic models are commonly used in engineering applications because of their ability to provide robust estimates of flood inundation depth and extent at high temporal and spatial resolutions. However, due to the high computational costs, 2D models are not suited to many applications such as real-time ensemble flood inundation forecasting or uncertainty analysis. Therefore, many models have been developed based on simplified hydraulic rules such as considering only the conservation of water mass. These models are generally faster than 2D models but have reduced accuracy, which is a problem in many studies where a fine simulation timestep is needed or flow dynamics are significant. Recently, emulation models have been developed for fast flood inundation modelling using data-driven techniques including artificial neural networks, machine learning classification models, and deep learning. These computationally efficient emulation models are found to have comparable accuracy with 2D models when used to simulate flood inundation water level or depth provided with rainfall or streamflow discharge inputs. However, most emulation models simulate flood water/depth for each grid cell in the modelling domain separately, which would significantly increase the computational costs when applied for large domains. To add to that, these models have been found to have reduced accuracy in data-scarce regions on the floodplain. To improve the performance of emulation models, the objective of this thesis is to develop computationally efficient flood inundation models using deep learning and new spatial representation methods, that can be used for fast flood inundation simulation on floodplains with various characteristics at high spatial and temporal resolutions. The major contributions of this thesis include: (1) the development of an emulator for rapid flood inundation modelling which incorporates a novel spatial reduction and reconstruction (SRR) method as well as long short-term memory (LSTM) deep learning models to efficiently estimate flood inundation depth and extent; (2) the development of a Python program for the SRR method for flood surface representation; (3) the development of a U-Net-based spatial reduction and reconstruction (USRR) method and one-dimensional convolutional neural network (1D-CNN) models to emulate flood inundation on flat and complex floodplains. In addition, an input selection structure is developed and validated in the architecture of the LSTM models to simplify the model development process and to reduce the effort required for real-world applications. Also, a comparison is carried out for the performance of the combined approaches of the SRR method and LSTM models, as well as the USRR method and 1D-CNN models in an application to a flat and complex floodplain. The comparison demonstrates the advantages of using the USRR-1D-CNN emulator for rapid modelling of flood inundation on flat floodplains with complex flow paths, while the SRR-LSTM emulator is more computationally efficient and suitable for application to steep floodplains. The flood inundation modelling schemes developed in this thesis provide fast estimates of flood inundation surfaces without a material loss of accuracy compared to 2D hydrodynamic models, useful for applications such as ensemble real-time flood forecasting and flood risk analysis. They have the potential to deepen our understanding of the impacts of input uncertainty on temporal and spatial patterns of flood inundation, and to facilitate improved flood risk management.
  • Item
    Thumbnail Image
    Enhancing the Sustainable Development Goals (SDGs): Integrating Resilience and Sustainability at the Local Level
    Assarkhaniki, Zahra ( 2022)
    In recent decades, urbanisation and population explosion have adversely affected communities’ resilience and sustainability. This problem is more prevalent in developing countries, which are among the most vulnerable communities left behind by most planning and development practices. Global efforts have been made to manage urbanisation in order to improve resilience to ongoing changes and sustainability of development and accordingly develop a framework to benchmark the two concepts. Even so, there is still a significant gap in the present knowledge at the intersection of resilience and sustainability, as well as the lack of a commonly accepted framework for measuring resilience itself. This prevents us from developing an integrated and commonly accepted framework for measuring resilience. The United Nations Sustainable Development Goals (SDGs) as a widely applied framework to measure the level of sustainable development at the country level has the potential to remove this discrepancy by considering the integration of resilience and measurement of sustainability. As elaborated in Chapter 1 (section 1.1), the framework is criticised for lacking a thorough measurement of resilience being more focused on sustainability. Thinking of the SDGs beyond “using the framework as the best possible approach” or “considering the SDGs as a failure because of its existing weaknesses”, this thesis acknowledges decades of work by the UN and many countries to build the SDGs framework and its worldwide application and at the same time the existence of some gaps and the opportunities for improvement. This research was thus intended to address one of the major criticisms of the Sustainable Development Goals, namely the lack of comprehensiveness in measuring resilience, as well as addressing shortfalls in current knowledge at the nexus of resilience and sustainability. In order to integrate a more comprehensive resilience measurement into the SDGs, this research looked at the possibilities for adding a more comprehensive set of resilience indicators into the SDGs. Besides, since the level of community resilience greatly depends on local characteristics, a methodological workflow for the SDGs’ localisation was urgently required. Hence, this research aimed to: firstly, integrate the quantification and measurement of sustainability and resilience in the context of the SDGs; and secondly, develop a methodological workflow to advance the approach to fit for purpose at the local level. To achieve the research aim, firstly, resilience dimensions and quantification have been conceptualised and a framework of baseline resilience indicators has been developed. Later, referring to the baseline indicators to measure each of the resilience dimensions, the aspects of resilience that have been overlooked among the SDGs have been defined. Accordingly, a pool of resilience indicators has been suggested to be integrated into the SDGs. Next, a localised workflow has been created, tested and evaluated to incorporate additional indicators that significantly contribute to the local SDG Index in the area of interest. The workflow for integrating resilience and sustainability in the SDGs at a local level contains three steps: 1) Assessing the pool of resilience and the SDGs indicators for data availability and application at the local level; 2) Aligning the indicators to with the local context if required; and 3) Analysing the significance of the indicators in measuring resilience/sustainability at the local level applying the Exploratory Regression method. To evaluate the developed workflow, the hypothesis of the research - that adding resilience indicators will enhance the effectiveness of the SDGs in measuring resilience at the local level - was tested. To implement the developed workflow, one of the major challenges in the SDGs utilisation is data availability and particularly in informal settlements, Jakarta, the capital city of Indonesia, with a big share of informal settlements has been selected as the case study. For testing the workflow, after executing steps one and two, each of the indicators and accordingly the local SDG Index has been calculated for each of the 286 sub-districts of Jakarta. As indicated, one main obstacle has always been data availability especially when it comes to informal settlements. Here, a novel method is proposed and used to generate the missing data on the location of these settlements applying machine learning (ML) techniques on the available spatial and statistical open data. Finally, the third step has been conducted and selected indicators have later been used to test the hypothesis of this research through developing the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. Results from the validation that support the hypothesis prove the effectiveness of the proposed workflow for the integration of resilience and sustainability at the local level in the SDGs. This research significantly contributes to the present knowledge about resilience and its quantification by conceptualising its dimensions, resolving the inconsistency in the terminology employed in this subject. As well as filling the gap of a common framework to measure resilience. The research also proposes resolutions for enhancing the Sustainable Development Goals as a globally adopted framework for measuring and monitoring sustainability. It does this by addressing the main criticism of the SDGs, which is the limitation in measuring resilience, and developing a workflow for SDGs’ localisation. In contrast to the current approaches for SDGs’ localisation that are mainly qualitative (see Chapter 8), the proposed method presents a quantitative robust workflow that can be followed in different cities at various jurisdictional levels. This research also presents a sample solution for coping with data availability issue while also proposing a novel method for the detection of informal settlements using freely accessible data. Furthermore, this research significantly contributes to the present knowledge in the domain of resilience and its quantification by conceptualising resilience dimensions, resolving inconsistencies in the subject’s terminology, and proposing a resolution for filling the gap of a common framework to measure resilience while also addressing a major criticism against the SDGs that is limitation in measuring resilience.
  • Item
    Thumbnail Image
    SHEAR BEHAVIOUR OF REINFORCED CONCRETE ELEMENTS: AN INSIGHT INTO SHEAR TRANSFER MECHANISMS IN CRACKED CONCRETE
    Jayasinghe, Thushara Prageeth ( 2022)
    Despite 70 years of investigations in understanding the shear behaviour of reinforced concrete members, it is again gaining attention among structural engineers as the recently issued Australian concrete design standard, AS 3600-2018, updated its shear provisions and ACI 318-19 unveiled its new one-way shear design equation. The shear behaviour of reinforced concrete elements is governed by several shear transfer mechanisms. Among them, the aggregate interlock is responsible for 50-70% of the ultimate shear transfer of cracked concrete elements. Despite its importance, a finite element model for shear transfer due to aggregate interlock considering realistic crack surfaces was still not developed. The complexity of developing a FE model lies due in the mesoscopic nature of the problem. In this study, a novel finite element approach is presented for evaluating shear transfer in crack concrete using realistic concrete crack surfaces. Concrete mesoscale models and zero-thickness cohesive elements were employed to develop the proposed method. Validation of the proposed FE models were conducted on two different experimental setups namely, small scale test and push-off test. The study comprises the evaluation of the surface roughness index of the cracked concrete surfaces. The proposed FE modeling approach demonstrated excellent performance against the most widely used analytical models and empirical equations for predicting the shear transfer in cracked concrete. Stress transfer in cracked concrete has been investigated since the 1970s, yet the existing code-based expressions for predicting shear transfer in cracked concrete were based on limited experimental data leading to insufficient prediction capabilities. Thus, this study further developed a machine learning-based framework for shear transfer in cracked concrete. The research outcomes present a novel finite element approach that is capable of evaluating stress transfer in cracked concrete and a machine learning-based framework to predict the maximum shear transfer in cracked reinforced concrete. The significance of the outcomes is that it enables to the evaluation of the stress transfer in crack concrete numerically while providing a clear pathway to solve the riddle of shear failures.