Infrastructure Engineering - Theses

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Harnessing renewable energy from geothermal pavements

Motamedi Ghahfarokhi, Yaser ( 2023)

Shallow geothermal energy as a kind of renewable energy resource has been attracting attention in recent years due to increasing appetite for turning to clean energy instead of conventional fossil-based fuels. Ground source heat pump (GSHP) systems address space heating and cooling demands by transferring heat from/into the ground through the pipes installed within the subsurface (called a ground heat exchanger, GHE). This reduces buildings’ energy consumption by virtue of higher efficiency than other conventional heating, ventilation, and air conditioning (HVAC) systems, finally reducing associated greenhouse gas emissions. To overcome the limitations of widespread use of this system which is due mainly to the high required capital cost of ground drilling and trenching, a more recent approach of energy geo-structure has been proposed, incorporating the geothermal pipes into underground structures such as piles and retaining walls to act as GHEs. This doctoral research contributes towards an improvement in this approach by investigating a new suggested energy geo-structure called “geothermal pavements”. In this technology geothermal pipes are embedded within the pavement layer at relatively shallow depths )~ 0.5 m) during the pavement constructions, leading to a significant reduction in capital costs by including the heat exchanger installation costs within the pavement earthworks. In this work, some aspects of geothermal pavements have been investigated experimentally and numerically. A large-scale field trial has been designed, constructed and instrumented in a carpark in Adelaide (South Australia) and has been subjected to several thermal response tests (TRT) to evaluate the geothermal pavements in terms of ground thermal response during the test, thermal properties of the ground, heat transfer between the heat exchanger and surrounding medium, and the effect of weather condition and seasonal variations on the TRT results. In addition to experimental investigations, a numerical study has been conducted to accurately simulate the geothermal pavements and predict its performance. A 3D FEM model has been developed and validated to determine the coefficient of performance (COP) of the system and the effect of surface boundary conditions on them. Moreover, a parametric study has been performed on design parameters to define the optimal design of geothermal pavements for the case study in Adelaide. The results showed that even though the relatively shallow burial depth of the heat exchanger affects the performance, the well-designed geothermal pavements can address the heating and cooling demands of nearby buildings. The effect of daily and seasonal weather variations on the TRT results was apparent while the TRT results are still useful to determine the thermal properties of the ground and evaluate the heat transfer around the heat exchanger. The numerical model results depicted the importance of surface boundary conditions selection in geothermal pavements and the effect of that on the predicted COP. The optimum amount of selected design parameters including length of the pipes, burial depth and spacing between the pipes were also determined when the maximum COP could be obtained.
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.
Improving understanding of energy retaining walls by field investigation and numerical modelling

Zhong, Yu ( 2023)

A transition from fossil fuels to renewable energy has received unprecedented momentum for the past decade, with its recognised significance in energy security, the climate, the environment, the economy and social cohesion. Geothermal energy is recognised as a clean and renewable energy source to contribute towards a sustainable and green future greatly. Ground Source Heat Pump (GSHP) systems, also known as shallow geothermal systems, directly harvest geothermal energy to provide heating and cooling. These systems utilise the ground as the heat source and heat sink by incorporating ground heat exchangers (GHEs), which are made of a series of absorber pipes with circulating heat carrier fluid buried underground. As the ground temperature tends to stabilise after the first few meters depth, GSHP systems can provide higher efficiency and reliability under extreme weather conditions than air source heat pumps and other heating and cooling systems in general. However, constructing GHEs in traditional ways requires purposely built boreholes or trenches for placing the pipes, which results in relatively high capital investment in GSHP systems. Energy-geostructures are economical alternatives to traditional GHEs, where the absorber pipes are embedded into underground structural elements primarily built for structural purposes, such as piles, retaining walls, tunnel linings, and pavements. However, additional constraints associated with energy geostructures introduce new challenges for GSHP system analyse and design. This doctoral research focuses on energy retaining walls (EWs), a relatively new energy geostructure that could be adopted in basements, underground carparks and metro stations. Current studies on EWs are still limited. How EWs could be effectively designed and utilised remains an outstanding question. This thesis addresses this issue through comprehensive field investigations combined with advanced finite element modelling. This thesis firstly acknowledged that the field monitoring and testing data regarding the thermal and thermo-mechanical behaviours of EWs are scarce. To fill the knowledge gap, full-scale field thermal testing and monitoring on a well-instrumented pilot soldier piled wall section located in an under-construction metro station as part of the AUD 11 billion Metro Tunnel Project in Melbourne, Australia, were conducted during this PhD journey. This field study provides unique and insightful information on both thermal and thermo-mechanical behaviours of EWS, enabling broader lessons to be shared with the scientific and engineering communities. Furthermore, the collected data form part of a rare dataset suitable for validating analytical and numerical models for EWs. Following the completion of the Melbourne Metro Tunnel project field study, detailed numerical finite element models have been developed for EWs to complement a better understanding of the underlying heat transfer mechanism and assess the thermal performance of EWs under various conditions. The models were thoroughly validated against the collected field data. The numerical results showed an excellent agreement with the field data under different conditions. Numerical analyses were first concentrated on the effect of pile spacing within energy piled retaining walls. The results suggest that the thermal performance of energy piled walls could be negatively impacted by the thermal interference among closely spaced thermo-active piles. This study recognises that actual design pile spacing is unlikely to be affected by the geothermal design but quantifying the effect of pile spacing can help in justifying how many piles may be thermally activated for a typical building at relatively close spacings. The second numerical analysis in this research took a closer look at the long-term heat transfer mechanism of full-scale EWs and surrounding ground with/without groundwater flow. The work demonstrated that the crucial role of groundwater on the thermal performance of EWs, the natural convective heat transfer of groundwater flow not only yields great importance for the thermal efficiency, resilience, and capacity of ground source heat pump systems but also significantly influences the temperature distribution of the ground. The thermal accumulation penalty should be carefully considered in large-scale GSHP system deployment, suggesting that obtaining reliable hydrogeological information, specifically on the groundwater flow velocity and direction, are crucial for accurate predictions and optimal design for energy retaining walls and large-scale GSHP installations. The final area of this research relates to the fact that numerical modelling of energy retaining walls is computationally expensive and less accessible to engineering designs. To tackle this shortcoming, a comprehensive parametric analysis\ was performed concerning the effects of key variables that govern the thermal performance of EWs, including ground temperature, groundwater flow velocities, the ground thermal conductivities, the pile embedment depth ratio, the convective heat transfer coefficient of the internal walls and slabs, and the load distribution. Initial design charts were developed to ease the preliminary assessment of the thermal potential from energy piles and energy piled walls based on specific ground conditions and thermal load distribution profiles, contributing towards more efficient and accessible design approaches. Altogether, each chapter provides valuable findings and knowledge on the energy retaining wall system aspects. The learnings gained from this research are expected to provide more confidence in the design of EWs and enhance the uptake of GSHP systems, which ultimately may bring us a step closer to a sustainable energy future.
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.
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.
Development of patient-specific rehabilitation strategies for distal radius fracture healing

Liu, Xuanchi ( 2023)

Distal radius fracture (DRF) is one of the most common types of orthopaedic injury. DRF could cause considerable physical and psychological morbidity in patients and bring severe societal effects. Therefore, the development of a cost-effective treatment plan to recover the radius bone structure and hand functions becomes necessary. During the past decades, internal fixation using the volar locking plate (VLP) has become a standard surgical process among orthopaedic surgeons because it could result in superior clinical healing outcomes. Besides, the fracture treatments with VLP allow physiotherapists to design early rehabilitation programs that help patients return to everyday life as soon as possible. However, current surgical strategies and post-operative exercises are developed based on clinical experiences and subjective judgement (i.e., trials and errors) rather than a thorough understanding of the underlying mechano-biology and biomechanics favourable to DRF healing. Hence, this thesis aims to address this research gap by systematically investigating the healing mechano-microenvironment under patient-specific clinical conditions. In this research, ex-vivo experiments were firstly conducted to study the biomechanical performance of the new-generation titanium VLP (VRP 2.0 and VRP 2.0+, Austofix, SA, Australia), which employs composite radius sawboneses, universal testing machines and advanced optical cameras. Then, a series of innovative 3D computational models were developed based on the theory of porous media to simulate the DRF healing processes. In addition, probabilistic approaches and machine learning algorithms were exploited to improve the effectiveness and efficiency of the simulation processes. It demonstrates that the model predictions can reproduce the experimental observations reasonably well. After validation, the model was implemented to explore the influence of different fracture geometries and VLP fixation configurations in healing outcomes. The effects of loading conditions on healing, such as how the physiologically relevant quasi-static loading influence mechano-regulated cell differentiation and tissue formation within early fracture callus, were also investigated. In addition, the impact of uncertainty in these clinical conditions on healing was studied using engineering reliability analysis. The results show that the developed model can successfully identify the optimal balance between the mechanical stability of VLP and the mechanobiology at the fracture site. Furthermore, the influence of physiologically relevant dynamic loading on the early healing microenvironment at the fracture site was studied to explore the synergistic effect of biomechanical and biochemical stimuli on the fate of progenitor cells. Finally, the time-dependent indirect healing pathway was simulated using mechano-regulation theory and strain-controlled angiogenesis. The models could be used to estimate the maximum allowable gripping strength under patient-specific conditions. Machine learning algorithms were combined with the model to transition simulation results to clinical use in real time. Through a systematic study, this research has enabled better comprehend the healing microenvironment of DRF treated with VLP. The research outcomes provide several valuable clinical suggestions for the treatment and rehabilitation of DRF. The following are some major conclusions: 1) During the surgical operation, more attention should be paid to the quality of fracture reduction compared to the VLP configuration. 2) For Smith fractures, a medium gap size after fracture reduction could benefit indirect healing, whereas a large fracture geometry under Colles fractures should be avoided because of the potential risk of delayed healing. 3) In the early stage of fracture healing, therapeutic hand-gripping exercises could benefit cartilaginous callus growth and endochondral ossification. Forearm rotation could hinder cartilage formation and slow the healing process. In addition, wrist flexion and extension could bring excessive fibrous connective tissues and cause fracture non-union. 4) As healing progresses, continuous daily hand gripping at medium speed (e.g., twice per second) could encourage secondary healing. By contrast, the healing progression could be hindered if the speed of exercises is too fast. In summary, this thesis proves the potential of numerical models in studying the complicated healing processes of metaphyseal bone fractures. The research represents the first step towards a full understanding of the optimum healing mechanic-biology and biomechanics under various macro clinical factors. Importantly, it provides the scientific basis for the development of innovative patient-specific rehabilitation strategies for distal radius fracture healing.
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.
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.
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.
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.

Infrastructure Engineering - Theses

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