yes / This paper presents a methodology for fault detection, fault prediction and fault isolation based on the integration of hidden Markov modelling (HMM) and Bayesian networks (BN). This addresses the nonlinear and non-Gaussian data characteristics to support fault detection and prediction, within an explainable hybrid framework that captures causality in the complex engineered system. The proposed methodology is based on the analysis of the pattern of similarity in the log-likelihood (LL) sequences against the training data for the mixture of Gaussians HMM (MoG-HMM). The BN model identifies the root cause of detected/predicted faults, using the information propagated from the HMM model as empirical evidence. The feasibility and effectiveness of the presented approach are discussed in conjunction with the application to a real-world case study of an automotive exhaust gas Aftertreatment system. The paper details the implementation of the methodology to this case study, with data available from real-world usage of the system. The results show that the proposed methodology identifies the fault faster and attributes the fault to the correct root cause. While the proposed methodology is illustrated with an automotive case study, its applicability is much wider to the fault detection and prediction problem of any similar complex engineered system.

Ultrasound directed self-assembly (DSA) relies on the acoustic radiation force associated with a standing ultrasound wave to organize particles dispersed in a fluid medium into specific patterns. State-of-the-art ultrasound DSA methods can only organize particles into (quasi-)periodic patterns, limited by the use of single-frequency ultrasound wave fields. Acoustic holography and acoustic waveguides provide alternatives to assembling complex patterns of particles, but generally provide low spatial accuracy and are not re-configurable because they require custom hardware for each specific pattern of particles, which is impractical. We introduce multi-frequency ultrasound wave fields to organize particles in non-periodic patterns. We theoretically derive and experimentally validate a solution methodology to determine the operating parameters (frequency, amplitude, phase) of any number and spatial arrangement of ultrasound transducers, required to assemble spherical particles dispersed in an inviscid fluid medium into any specific two-dimensional pattern. The results show that multi-frequency ultrasound DSA enables the assembly of complex, non-periodic patterns of particles with substantially fewer ultrasound transducers than single-frequency ultrasound DSA, and without incurring a penalty in terms of accuracy. The results of this work fundamentally transform the state-of-the-art knowledge of ultrasound DSA. Multi-frequency ultrasound wave fields enable a near-unlimited complexity of patterns of particles that can be assembled, increasing the relevance of the technology to practical implementation in engineering applications such as manufacturing of engineered composite materials that derive their properties from the spatial organization of the filler in the matrix material. Although this work focuses specifically on ultrasound wave fields, the theoretical model is valid for all wave phenomena. / Master of Science / Ultrasound directed self-assembly (DSA) is the process where particles dispersed in a fluid medium assemble into specific patterns due to their interactions with a sound wave and/or other particles. Current ultrasound DSA methods use a single-frequency ultrasound wave to assemble particles into specific patterns, which creates repeating patterns within the fluid medium. Other methods of assembling particles that allow for more complex, non-repeating patterns generally provide low spatial accuracy and do not allow dynamically changing the pattern as they require custom hardware for each specific pattern of particles, rendering these methods impractical. We use many ultrasound waves each with a different frequency to organize particles into complex, non-repeating patterns, which we call multi-frequency ultrasound DSA. We theoretically derive and experimentally validate a method that allows us to assemble any specific two-dimensional pattern of particles using multi-frequency ultrasound DSA. The results show that multi-frequency ultrasound DSA enables the assembly of complex, non-repeating patterns of particles with substantially resources than single-frequency ultrasound DSA, and without incurring a penalty in terms of accuracy. Multi-frequency ultrasound DSA enables a near-unlimited complexity of patterns of particles that can be assembled, increasing the relevance of the technology to practical implementation in engineering applications.

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Yes / This paper explores the development of the most suitable machine learning models for predicting the bending capacity of steel and FRP (Fiber Reinforced Ploymer) bars hybrid reinforced ECC (Engineered Cementitious Composites)-concrete composite beams. Five different machine learning models, namely Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), Random Forest (RF), and Extremely Randomized Trees (ERT), were employed. To train and evaluate these predictive models, the study utilized a database comprising 150 experimental data points from the literature on steel and FRP bars hybrid reinforced ECC-concrete composite beams. Additionally, Shapley Additive Explanations (SHAP) analysis was employed to assess the impact of input features on the prediction outcomes. Furthermore, based on the optimal model identified in the research, a graphical user interface (GUI) was designed to facilitate the analysis of the bending capacity of hybrid reinforced ECC-concrete composite beams in practical applications. The results indicate that the XGBoost algorithm exhibits high accuracy in predicting bending capacity, demonstrating the lowest root mean square error, mean absolute error, and mean absolute percentage error, as well as the highest coefficient of determination on the testing dataset among all models. SHAP analysis indicates that the equivalent reinforcement ratio, design strength of FRP bars, and height of beam cross-section are significant feature parameters, while the influence of the compressive strength of concrete is minimal. The predictive models and graphical user interface (GUI) developed can offer engineers and researchers with a reliable predictive method for the bending capacity of steel and FRP bars hybrid reinforced ECC-concrete composite beams.

The complexity of engineered systems has increased remarkably to meet customer needs. In the continuously growing global market, it is essential for engineered systems to keep their productivities which can be achieved by higher reliability and availability. Integrated health management based on diagnostics and prognostics provides significant benefits, which includes increasing system safety and operational reliability, with a significant impact on the life-cycle costs, reducing operating costs and increasing revenues. Characteristics of complex systems such as nonlinearity, dynamicity, non-stationarity, and non-Gaussianity make diagnostics and prognostics more challenging tasks and decrease the application of classic reliability methods remarkably – as they cannot address the dynamic behaviour of these systems. This research has focused on detecting, predicting and isolating faults in engineered systems, using operational data with multifarious data characteristics. Complexities in the data, including non-Gaussianity and high nonlinearity, impose stringent challenges on fault analysis. To deal with these challenges, this research proposed an integrated data-driven methodology in which hidden Markov modelling (HMM) and Bayesian network (BN) were employed to detect, predict and isolate faults in a system. The fault detection and prediction were based on comparing and exploiting pattern similarity in the data via the loglikelihood values generated through HMM training. To identify the root cause of the faults, the probability values obtained from updating the BN were used which were based on the virtual evidence provided by HMM training and log-likelihood values. To set up a more accurate data-driven model – particularly BN structure – engineering analyses were employed in a structured way to explore the causal relationships in the system which is essential for reliability analysis of complex engineered systems. The automotive exhaust gas Aftertreatment system is a complex engineered system consisting of several subsystems working interdependently to meet emission legislations. The Aftertreatment system is a highly nonlinear, dynamic and non-stationary system. Consequently, it has multifarious data characteristics, where these characteristics raise the challenges of diagnostics and prognostics for this system, compared to some of the references systems, such as the Tennessee Eastman process or rolling bearings. The feasibility and effectiveness of the presented framework were discussed in conjunction with the application to a real-world case study of an exhaust gas Aftertreatment system which provided good validation of the methodology, proving feasibility to detect, predict, and isolate unidentified faults in dynamic processes.

This dissertation is focus on the study of solid-electrolyte interphases (SEIs) on advanced lithium ion battery (LIB) anodes. The purposes of this dissertation are to a) develop a methodology to study the properties of SEIs; and b) provide guidelines for designing engineered SEIs. The general knowledge gained through this research will be beneficial for the entire battery research community.

Energy related geo-systems involve a wide range of engineering solutions from energy piles to energy geo-storage facilities and waste repositories (CO₂, nuclear). The analysis and design of these systems require proper understanding of geo-materials, their properties and their response to extreme temperature and high stress excitations, the implications of mixed-fluid conditions when contrasting fluid viscosities and densities are involved, the effect of static and cyclic coupled hydro-thermo-chemo-mechanical excitations, and rate effects on the response of long design-life facilities. This study places emphasis on thermal geo-systems and associated physical properties. Uncemented soils and rocks are considered. The research approach involves data compilation, experimental studies and analytical methods. Emphasis is also placed to engineer geomaterials in order to attain enhanced performance in energy geo-systems. The thermal conductivity and stiffness of most geomaterials decrease as temperature increases but increase with effective stress. This macroscale response is intimately related to contact-scale conduction and deformation processes at interparticle contacts. Pore-filling liquids play a critical role in heat conduction as liquids provide efficient conduction paths that can diminish the effects of thermal contact resistance. Conversely, grains and fluids can be selected to attain very low thermal conductivity in order to create mechanically sound thermal barriers. In the case of rock masses, heat (and gas) recovery can be enhanced by injecting fluids at high pressure to cause hydraulic fractures. Scaled experiments reveal the physical meaning of hydraulic fractures in pre-structured rocks (e.g., shale) and highlight the extensive self-propped dilational distortion the medium experiences. This result explains the higher production rate from shale gas and fractured geothermal reservoirs that is observed in the field, contrary to theoretical predictions.

Metal-based engineered nanomaterials (ENMs) have potential to revolutionize diagnosis, drug delivery and manufactured products, leading to greater human ENM exposure. It is crucial to understand ENM pharmacokinetics and their association with biological barriers such as the blood-brain barrier (BBB). Physicochemical parameters such as size and surface modification of ENMs play an important role in ENM fate, including their brain association. Multifunctional ENMs showed advantages across the highly regulated BBB. There are limited reports on ENM distribution among the blood in the brain vasculature, the BBB, and brain parenchyma. In this study, ceria ENM was used to study the effect of size on its pharmacokinetics. Four sizes of ceria ENMs were studied. Five nm ceria showed a longer half-life in the blood and higher brain association compared with other sizes and 15 and 30 nm ceria had a higher blood cell association than 5 or 55 nm ceria. Because of the long circulation and high brain association of 5 nm ceria compared with other sizes, its distribution between the BBB and brain parenchyma was studied. The in situ brain perfusion technique showed 5 nm ceria (99%) on the luminal surface of the BBB rather than the brain parenchyma. For biomedical applications in the central nervous system (CNS), it is vital to develop stable and biocompatible ENMs and enhance their uptake by taking advantage of their unique properties. Cross-linked nanoassemblies entrapping iron oxide nanoparticles (CNA-IONPs) showed controlled particle size in biological conditions and less toxicity in comparison to Citrate-IONPs. CNA-IONPs considerably enhanced MRI T2 relaxivities and generated heat at mild hyperthermic temperatures (40 ~ 42°C) in the presence of alternating magnetic field (AMF). Numerous researchers showed mild whole body hyperthermia can increase BBB permeability for potential brain therapeutic application. Compared to conventional hyperthermia, AMF-induced hyperthermia increased BBB permeability with a shorter duration of hyperthermia and lower temperature, providing the potential to enhance IONP flux across the BBB with reduced toxicity. Overall, ENMs with optimized physicochemical properties can enhance their flux across the BBB into the brain with desirable pharmacokinetics, which provide great potential for diagnosis and therapy in the CNS.

Thesis (MScEng (Civil Engineering))--University of Stellenbosch, 2005. / 192 leaves on CD format, preliminary i-xii pages and numbered pages 1-135. Includes bibliography, list of figures and tables. / ENGLISH ABSTRACT: As a fibre reinforced material, engineered cementitious composite (ECC) has tough, strain-hardening behaviour in tension despite containing low volumes of fibres. This property can be brought about by developments in fibre, matrix and interfacial properties. Poly Vinyl Alcohol (PVA) fibre has been developed in recent years for ECC, due to its high tensile strength and elasticity modulus. However, the strong interfacial bond between fibre surface and matrix is a challenge for its application. This study focuses on the tailoring of matrix and fibre/matrix interfacial properties by cement replacement with fly ash (FA) and Ground Granulated Corex Slagment (GGCS). In this study the direct tensile test, three point bending test, micro-scale analysis, such as X-Ray Fluorescence Spectrometry analysis (XRF), Scanning Electron Microscope (SEM), are employed to investigate the influence of cement replacement, aging, Water/Binder (W/B) ratio, workability on ECC behaviour. This study has successfully achieved the aim that cement replacement by FA and GGCS helps to improve the fibre/matrix interfacial properties and therefore enhances the ECC tensile behaviour. Specifically, a high volume FA-ECC has stable high tensile strain capacity at the age of 21 days. This enables a constant matrix design for the investigation of other matrix influences. The Slag-ECC has a higher tensile strength but lower tensile strain capacity. The combination of FA and GGCS, moderate tensile strength and strain capacity is achieved Both tensile tests and Micro-scale analyses infer that the high volume FA-ECC has an adhesive type fibre/matrix interfacial interaction, as opposed to the cohesive type of normal PVA fibre-ECC. The different tensile behaviour trend of steel fibre-ECC and PVA fibre-ECC with the FA content is presented and discussed in this research. The investigations of aging influence indicate that the high volume FA-ECC has a beneficial effect on the properties of the composite at an early stage. However, at a high age, it has some difficulty to undergo multiple cracking and then leads to the reduction of tensile strain capacity. The modified mix design is made with the combination of FA and GGCS, which successfully increases the interfacial bond and, thereby, improves the shear transfer to reach the matrix crack strength. Therefore, an improved high age tensile behaviour is achieved. The W/B and fresh state workability influence investigations show that the W/B can hardly affect the tensile strain at early age. However, the workability influences on composite tensile strain significantly, because of the influence on fibre dispersion. Other investigations with regard to the hybrid fibre influences, the comparison of bending behaviours between extruded plate and cast plate, the relation between bending MOR and tensile stress, and the relation between compression strength and tensile strength contribute to understand ECC behaviour. / AFRIKAANSE OPSOMMING: As ‘n veselversterkte materiaal, het ontwerpte sementbasis saamgestelde materiale, taai vervormingsverhardingseienskappe in trek, ten spyte van lae veselinhoud. Hierdie eienskap word bewerkstellig, deur ontwikkelings in vesel, matriks en tussenveselbindingseienskappe. Poli-Viniel Alkohol (PVA) vesels is ontwikkel vir ECC, as gevolg van die hoë trekkrag en hoë modulus van hierdie veseltipe. Die sterk binding tussen die PVA-veseloppervlak en die matriks is egter ‘n uitdaging vir sy toepassing. Hierdie studie fokus op die skep van gunstige matriks en vesel/matriks tussenvesel-bindingseienskappe deur sement te vervang met vlieg-as (FA) en slagment (GGCS).In hierdie navorsing is direkte trek-toetse, drie-punt-buigtoetse, mikro-skaal analise (soos die X-straal ‘Fluorescence Spectrometry’ analise (XRF) en Skanderende Elektron Mikroskoop (SEM))toegepas. Hierdie metodes is gebruik om die invloed van sementvervanging,veroudering, water/binder (W/B)-verhouding en werkbaarheid op die meganiese gedrag van ECC te ondersoek.Die resultate van hierdie navorsing toon dat sementvervanging deur FA en GGCS help om die vesel/matriks tussenveselbindingseienskappe te verbeter. Dus is die ECC-trekgedrag ook verbeter. Veral ‘n hoë volume FA-ECC het stabiele hoë trekvervormingskapasiteit op ‘n ouderdom van 21 dae. Dit bewerkstellig ‘n konstante matriksontwerp vir die navorsing van ander matriks invloede. Die Slag-ECC het ‘n hoër treksterkte, maar laer trekvervormingskapasiteit. Deur die kombinasie van FA en GGCS word hoë treksterkte, sowel as gematigde vervormbaarheid in trek verkry. Beide trektoetse en mikro-skaal analise dui aan dat die hoë volume FA-ECC ‘n adhesie-tipe vesel/matriks tussenvesel-bindingsinteraksie het, teenoor die ‘kohesie-tipe van normale PVA vesel-ECC. Die verskille in trekgedrag van staalvesel-ECC en PVA vesel-ECC ten opsigte van die FA-inhoud is ondersoek en word bespreek in die navorsing. Die navorsing toon verder dat die hoë volume FA-ECC goeie meganiese eienskappe het op ‘n vroeë ouderdom. Op hoër ouderdom word minder krake gevorm, wat ‘n verlaging in die trekvervormingskapasiteit tot gevolg het. Met die kombinasie van FA en GGCS, word die vesel-matriksverband verhoog, waardeur ‘n verbetering in die skuifoordrag tussen vesel en matriks plaasvind. Verbeterde hoë omeganiese gedrag word daardeur tot stand gebring. Navorsing ten opsigte van die invoed van die W/B en werkbaarheid dui daarop dat die W/B slegs geringe invloed het op die trekvormbaarheid, terwyl die werkbaarheid ‘n dominerende rol speel in hierdie verband.Verdere studies sluit in die invloed van verskillende vesels, die vergelyking van die buigingsgedrag van geëkstueerde plate en gegote plate, die verhouding tussen buigsterkte en treksterkte, en die verhouding tussen druksterkte en treksterkte dra by tot beter begrip van die gedrag van ECC.