Publication Search Results

Now showing 1 - 7 of 7
  • (2020) Kim, Seungbeom
    Cyanobacteria, also referred to as blue-green algae, are aquatic and photosynthetic, that is, that live primarily in fresh water and salt water. In addition to unattractive colour and smelly odour, abundance of cyanobacteria worsens water-quality and generates toxins that can harm humans and animals alike. Lack of enough data, its independence across multiple sampling time steps, as well as the presence of more than one causative factors, each with different levels of influence on the response, has resulted in limited progress in the development of generalized Cyanobacteria modelling and prediction frameworks. In this thesis, using a few key dominant factors, relatively practical and universally applicable two models for predicting the cyanobacterial bloom have been developed. The first model is a binary model and forecasts the occurrence/non-occurrence of cyanobacterial bloom at a given time step conditional on the dominant environmental variables and the cyanobacteria concentration at the preceding time step. The bacterial growth dynamic to the model is included by defining the weight functions which quantify the importance assigned to the key environmental variables namely, temperature, velocity and nutrients. A probabilistic model can yield a distribution of possible outcomes and therefore helps not only to understand the degree of outcome but also to make a relevant solution with uncertainty. Following this, a two-stage probabilistic model has been developed. In the first stage, cyanobacteria occurrences are generated using a first-order conditional Markov model. The conditioning vector includes the cyanobacterial count on the preceding time step and a few environmental variables. On occasions where the first stage model predicts the occurrence of cyanobacteria, the second stage model generates cyanobacterial cell counts using a nonparametric kernel density approach assuming first-order Markovian conditional dependence. As a final stage of this thesis, a few scenarios for controlling cyanobacterial bloom growth are assessed in terms of changes in the environmental variables and financial implications. Both developed models provide promising results and offer the capability of applying them to other areas. The suggested countermeasure provides an interesting and economically feasible solution to deal with cyanobacterial bloom issue

  • (2022) Chen, Yuhui
    Ride-sourcing services are rapidly spreading around the world. The ride-sourcing service refers to a point-to-point on-demand ride service operated by various companies, which organize and coordinate drivers using their vehicles to provide passengers with ride services. How ride-sourcing services and public transport are interacting with each other and thus yielding system-wide impacts have not received sufficient attention. This thesis extends the literature by proposing multi-class, multi-modal traffic assignment models to optimize the transport system with the presence of ride-sourcing and public transport services. The first part of the thesis develops a stylized model with a simple network with single origin-destination pair in order to analytically examine the mode choice behavior of travelers and the operation strategies of a public transport operator and a ride-sourcing operator. In such a multi-modal system, users may travel by bus, train, or ride-sourcing service. In particular, we develop a tractable bi-level model that quantifies the user equilibrium travel choices in the lower-level, where the travel choice equilibrium can be formulated as a variational inequality problem, and optimizes the operation strategies of the public transport operator that aims to minimize total system cost and the ride-sourcing operator that aims to maximize its profit in the upper-level. The existence and uniqueness of the multi-modal travel choice equilibrium are also analyzed. How the operation decision variables might affect users' mode choices and system performance is investigated both analytically and numerically. The second part of the thesis extends the stylized model to a general network model, which includes also solo-driving, and multiple OD pairs to depict a more realistic problem setting. The general network model is applied on a case study in the context of Sydney. The existence and uniqueness are also investigated for the general network model. The method of Frank-Wolfe combined with diagonalization is applied to generate numerical solutions, and illustrate the analytical observations and generate further understanding. The results show that the total system cost can be reduced while the profit of the ride-sourcing company can be increased under appropriate operating strategies of the public transport operator and the ride-sourcing operator.

  • (2020) Yang, Bing
    Social media data have been used in many studies in the recent years. Compared to the traditional household travel survey data, social media data have a lower cost, and they can be obtained from abundant sources. However, several pre-processing tasks are required before social media data can be used for mobility analyse. For instance, distinguishment needs to be made between location and activity. In this thesis, text analytics, machine learning, and “Tweet Block” are applied in order to differentiate between location and activity and to extract more accurate information from social media data which can then be used for mobility analyse. In Section 3, the focus is to extract and analyse users’ movement and lifestyle. Unlike the state-of-the-practice, this research clearly distinguishes between location and activity. Text mining technique was applied to identify location and activity information respectively, and a clustering algorithm was applied to analyse the lifestyle of users. The strict distinguish between activity and location led to a result that the identified data is limited compared to traditional ways of labelling. To solve this problem, the information extracted from data was enriched by applying a method called “Tweet Block”. Tweet Block enable to identify 1,745 location and 98 activity which were not identified in text mining process. With the enriched data in hand, a method was purposed to infer information of user’s movement from the data point that is previously unusable (i.e. a single record from a day.) The average generated trip rate using this method was increased by 26%-50% compared to the method used in previous research. Travelling track was also generated to analyse the movement of these users. In Section 4, the primary purpose is to build a valid activity prediction model from the data. Machine learning algorithms were applied to build an activity prediction model from the data. Land use data were overlapped to the original data set, which acted as a supportive data to location information. Random Forest (RF) and Neural Network (NN) algorithms were used to build models and NN models were kept after model selection. A Stratified K-fold cross-validation was used to validate the model.

  • (2020) Liang, Gan
    Sydney Harbour sediments are severely contaminated with polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) as a result of the intensive production and utilization of organohalide products in Sydney. These highly toxic and recalcitrant contaminants leached into Sydney Harbour with chemical waste that was landfilled on the banks of Homebush Bay and readily accumulated in marine sediments, representing a risk to the ecosystem in Sydney Harbour and human health. In 2017, PCDD/Fs in the harbour sediments were quantified and compared with historical data, with little change being observed over the decade compared with the study of Birch et al. (2007). This suggests that the risks associated with the contamination will remain, until a practical strategy for PCDD/F remediation is developed. Previous studies have shown that microorganisms are able to transform PCDD/Fs, with the potential to detoxify and eliminate these compounds. The goal of this study was to survey the feasibility of applying bioremediation technologies for PCDD/F detoxification in Sydney Harbour sediments. DNA sequencing revealed the presence of bacteria closely related to known PCDD/F degrading microbes belonging to the Dehalococcoides genus in the harbour sediments. Anaerobic enrichment cultures supplied with perchloroethene (PCE) as terminal electron acceptor stimulated the reductive dechlorination of the most toxic dioxin 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) to 2,3,7-trichlorodibenzo-p-dioxin (2,3,7-TriCDD) and the most abundant dioxin octachlorodibenzo-p-dioxin (OCDD) to its hepta- and hexa-chlorinated congeners. This is consistent with the existence of microbes capable of reductively dechlorinating PCDD in Sydney Harbour. The reasons for the lack of significant in situ PCDD/F biotransformation were considered. With 4500 and 10 times higher than the environmental concentrations, respectively, TCDD and OCDD partially but reversibly inhibited the microbial dechlorination activity. The low aqueous solubility of TCDD and OCDD likely limited the capacity of PCDD/F respiring bacteria dechlorinating these compounds and with the use of a biosurfactant lecithin, the microbial dechlorination of TCDD and OCDD was enhanced. These findings demonstrated that applying indigenous microorganisms maybe part of the remedy for Sydney Harbour PCDD/F contamination. Furthermore, this study explored the potential of using a known PCDD dechlorinating bacteria Dehalococcoides mccartyi strain CBDB1 and sulfidized nanoscale zerovalent iron (S-nZVI) in addressing PCDD degradation in conditions found in Sydney Harbour. The tolerance of strain CBDB1 to seawater environments was determined, with a view to its deployment into Sydney Harbour’s contaminated sediments. Additionally, S-nZVI did not react with TCDD and OCDD, but it was capable of supplying H2 and conditioning the redox potential necessary for strain CBDB1 organohalide respiration, with the potential to assist strain CBDB1 dechlorinating PCDD in Sydney Harbour.

  • (2021) Freeman, Elizabeth
    This research focuses on the dynamic motions of piled floating pontoons and their impact on a standing person’s stability. Piled floating pontoons are public access structures that provide a link between land and sea. There is limited useful data on the dynamic motions (acceleration and rotation) of piled floating pontoons to wave excitation. Similarly, there are no design standards specific to floating pontoons specifying suitable motion limits in order to maintain the postural stability of users. This research first proposes a set of Safe Motion Limits (SML) in the form of lateral, vertical and rotational accelerations in order to maintain a standing person’s stability. Both laboratory and prototype testing have been undertaken in order to record the motion response of piled floating pontoons, resulting from boat wake. The motions recorded are compared against the proposed Safe Motions Limits (SML), to ascertain the impact on a standing person’s postural stability. Extensive laboratory-scale physical model experiments were undertaken at UNSW Water Research Laboratory. Two varying width piled floating pontoons of variable draft, subjected to regular boat wake conditions were tested. Five Inertial Measurement Units (IMUs), were positioned on each pontoon and used to record accelerations and rotations. Observed accelerations and roll angles were dependent on beam to wavelength (B/L). Internal mass played a secondary role, with larger mass structures resulting in overall lower accelerations for similar B/L ratios. Increasing draft improved attenuation performance, most notably at a wave period of 3 seconds. As draft increased peak heave acceleration decreased however the percentage exceedance of the lateral SML increased. Prototype testing documenting both pontoon motions and user perceptions of motion was undertaken with motions recorded exceeding the nominated SML and users conveying levels of discomfort. Importantly, results have revealed the complex interaction between the piles and pontoon that result in peak accelerations more than six times the nominated operational SML of 0.1g. Root-mean-square accelerations were observed to be more than three times greater than the nominated comfort limit (0.02g) and angles of rotation more than double what would be perceived as safe/comfortable (6 degrees) for the mild wave conditions tested.

  • (2023) Li, Jiacheng
    Soil consolidation refers to the process of stress changes and soil deformation due to loading and has extensive applications in geotechnical engineering disciplines. Past experimental and theoretical evidence have shown that soil consolidation varies with soil types and degree of saturation. Most of the previous studies were however involved only static loading of soil samples. In this study, one-dimensional consolidation tests with cyclic loading were carried out on three different proportions of Sydney sand – kaolin mixtures in laboratory to supplement the data in the literature, and to provide data to support the comparison and subsequent numerical simulation works. An advanced Constant-Rate-of-Strain (CRS) test apparatus capable of applying cyclic load on samples was used to be able to monitor the variation of settlement and excess pore water pressure in the samples accurately throughout the tests. The effects of the degree of saturation and the frequency and amplitude of the cyclic load on the settlement and pore water pressure during one-dimensional cyclic consolidation tests were thoroughly investigated for each mixture. The results indicated that the degree of saturation of soil considerably influences the settlement process, and that the frequency of cyclic loading has an effect on the pore water pressure generation and dissipation time of the saturated soil. For different mixtures, the soil settlement and pore water pressure generation vary considerably, depending on the soil mixture and test parameters. The result of the study provides valuable data for subsequent simulations and validations of various constitutive models.

  • (2023) Tian, Weizhe
    Architected lattice structures are designed in a periodic fashion and constructed with the Additive Manufacturing (AM) technique to achieve specific mechanical properties and maintain the relative lightweight, which have been utilized in the engineering sectors such as aerospace, biomedical and construction, etc. However, despite the numerous applications, studies incorporating the lattice structures into composite plates remain to be explored. Besides, it is still an open question for the stability of lightweight composite plates under dynamic loading. In addition, although the AM technique facilitates the fabrication of lattice structures, the induced manufacturing errors would significantly affect the performance of the composite plates. Therefore, this thesis presents a nonlinear dynamic analysis and a machine learning aided stochastic analysis to investigate the influence of the dynamic loadings and AM errors on the stability of composite plates respectively. For the dynamic analysis, four types of dynamic loads including sinusoidal, exponential, rectangular and damping are considered to simulate the real-life dynamic loading. The governing equation is built based on the first-order shear deformation theory and then resolved by the Galerkin method and the fourth-order Runge-Kutta methods. For the machine learning aided stochastic analysis, three commonly observed manufacturing errors including strut node dislocation, radius variation and waviness are considered. The analysis framework is established by adopting the Extended Support Vector Regression (X-SVR) for the AM error influence investigation. The investigations proposed in this thesis could be useful in the optimal design and the safety assessment for the lattice based composite structure, where in the dynamic analysis, the correlations between the dynamic buckling load and the crucial factors such as the geometry, relative density, initial imperfection and damping are established, which provides new insights into the role of structural design. While in the stochastic analysis, the potential influence of AM errors can be quantified with statistical information including the mean, standard deviation, probability density function (PDF) and cumulative density function (CDF) etc. The efficiency, robustness and other inherent features such as information update further highlight the applicability of the proposed AM error quantification scheme in real-world engineering applications.