Geotechnical engineering has a crucial role to play in enhancing sustainability due to its pivotal role in the construction process where potentially impacts are highest. Currently, there is a lack of methodologies for assessing geotechnical projects that truly encompass the three core pillars of sustainability. A robust system is required which offers an holistic approach that is both flexible and easily understood, whilst not being biased towards rewards or is prohibitively costly. In addition, ‘tool fatigue’, whereby a system is generated but never used, must be avoided. After a detailed evaluation of the systems available, the SPeAR® framework was selected. Following detailed discussion with a variety of practitioners, the methodology was significantly adapted to make it applicable to geotechnical problems and ensure that geotechnical engineers can understand and use it with relatively ease. The new version, called ‘GeoSPeAR’ in this thesis, allows for greater communication between masterplanning and geotechnical engineering via their common base, thus avoiding a potential barrier to greater adoption of more sustainable practices through the construction cycle. Three case studies demonstrated the assessment of the ‘GeoSPeAR’ methodology. These showed the practical application of the system and how this effectively supports geotechnical engineers in embedding sustainability into projects.

Motivated by internal fluidisation due to a leaking pipe, this thesis aims to achieve a deeper understanding of the leakage-soil interaction by numerical simulations. The coupled DEM (Discrete Element Method) – LBM (Lattice Boltzmann Method) technique has been regarded as a promising tool to efficiently provide detailed description of fluid-particle systems, especially with intensive fluid-particle interactions. Therefore, FPS-BHAM, a 2D DEM-LBM computer code is developed for this thesis. In this code, DEM models the soil as an assembly of circular particles, and LBM is employed for fluid flow simulations. The Immersed Moving Boundary (IMB) scheme is adopted for the treatment of fluid-particle interaction. Numerical results are validated against existing experimental findings. Different regimes of bed behaviour are demonstrated under various flow rates. The onset of fluidisation is studied by analytically deriving for the flow equation and fluidising pressure, which are compared with numerical solutions. Moreover, deeper insights are made into the post-fluidisation phenomenon. The mechanism underlying a stable cavity is explored, and how a cavity evolves with different factors is also investigated through parametric studies.

Cast iron water distribution pipes exist widely in UK and elsewhere. As these cast iron pipes were traditionally directly buried into local soil, where the soil is chemically aggressive (as in the case of some clays) corrosion of the pipes often occurs due to an electrochemical process, which both changes the pH environment and releases iron ions into the clay. This can cause chemical alteration to clay minerals and ‘corrosion products’, such as iron oxide, hydroxide and aqueous salts, to form in the soil. These chemical interactions are complex and time dependent, and can result in failure, and thus the conditions under which they occur need to be understood. Ground Penetration Radar (GPR) has been proposed for routinely detecting, assessing and monitoring buried cast iron pipes, and thus it is important to know how these chemical changes affect the electromagnetic properties of soil. A bespoke set of laboratory experiments was devised to simulate and accelerate (using electrokinetics) cast iron corrosion and ion migration processes in two types of clay: a relatively inactive Kaolin Clay and Oxford Clay, which has a mixed mineralogy. Tests were conducted for periods of up to 3 months using both inert electrodes and with a cast iron disc as the anode, and changes in the geotechnical (undrained shear strength, moisture content and Atterberg limits), geophysical (permittivity) and geochemical (iron content, pH and conductivity) properties were monitored. The conductivity and permittivity results were used in GPR simulations to investigate reported difficulties in detecting corroded cast iron pipes.

This research aims to characterise the aerodynamic flow around a container freight train and investigate how changing container loading configuration affects the magnitude of aerodynamic forces measured on a container. 1/25th scale moving-model freight train experiments were carried out at the University of Birmingham's TRAIN rig facility. Slipstream velocities and static pressure were measured to assess the influence of container loading. Aerodynamic loads were measured using a specifically designed on-board pressure monitoring system. A collation of full scale freight data from previous studies provided a tool for validation. Analysis of freight data found slipstream results could be presented as a series of flow regions. Differences in slipstream development and aerodynamic load coefficients were observed for differing container loading configurations. Velocity magnitudes in the nose region were larger than observed previously. Boundary layer growth stabilises rapidly for loading efficiencies greater than 50%, however, for less than 50% continual boundary layer growth was observed until after 100m when stabilisation occurs. Comparison of model and full scale data indicated Reynolds number independence. Analysis of TSI specifications found results lie close to, but do not break, existing limits. Aerodynamic load coefficients were shown to be characteristic of typical values for a 30° yaw angle.

Innovative human-computer interaction paradigms with minimum motor control provide realistic interactions and have potential for use in assistive technologies. Among the human modalities, the eyes and the brain are the two modalities with minimum motor requirements. Most of the existing assistive technologies based on tracking the eyes (such as electrooculography and videooculography) are intrusive, limited to the laboratory environment and restrictive or are not accurate enough for real-life applications. The same limitations apply to brain activity monitoring systems such as electroencephalography (EEG). In this research, the objective is to employ a less-intrusive, consumer-grade EEG headset designed for mobile applications to track the user’s eyes and reliably estimate focus of foveal attention (FoA). To this end, signal processing approaches are proposed in order to classify different types of eye movements and estimate FoA. The FoA estimation is then improved using the brain responses to flickering stimuli recorded in EEG data. Afterwards, the FoA estimation is again improved by proposing an automated method to remove eye-related artefacts from brain responses to the stimuli. Finally, the FoA estimation is best improved by extracting eye-movement classification and brain-response detection features from EEG data projected into independent sources.

Control charts are graphical tools that monitor and assess the performance of production processes, revealing abnormal (deterministic) disturbances when there is a fault. Simple patterns belonging to one of six types can be observed when a fault is occurring, and a Normal pattern when the process is performing under its intended conditions. Machine Learning algorithms have been implemented in this research to enable automatic identification of simple patterns. Two pattern generation schemes (PGS) for synthesising patterns are proposed in this work. These PGSs ensure generality, randomness, and comparability, as well as allowing the further categorisation of the studied patterns. One of these PGSs was developed for processes that fulfil the NIID (Normally, identically and independently distributed) condition, and the other for three first-order lagged time series models. This last PGS was used as base to generate patterns of feedback-controlled processes. Using the three aforementioned processes, control chart pattern recognition (CCPR) systems for these process types were proposed and studied. Furthermore, taking the recognition accuracy as a performance measure, the arrangement of input factors that achieved the highest accuracies for each of the CCPR systems was determined. Furthermore, a CCPR system for feedback-controlled processes was developed.

Road maintenance planning, an essential component of road asset management, preserves the integrity of road networks. Current state of the art pavement management systems exercise optimisation tools, pavement deterioration models, and intervention criteria to forecast the future maintenance requirements of a road network. However, using this current approach to pavement management, uncertainties associated with the failure of individual road sections may not always be accounted for explicitly, and therefore the susceptibility of a road network to failure is unknown. Predicting the probability of the end of life of a road pavement involves wholly understanding possible modes of failure and utilising suitable computational techniques, so that engineering knowledge can be well represented in data driven models. To this end, this thesis describes the development of a diagnostic approach that infers engineering knowledge into computational models, to quantify the probability and identify the most likely causes of failure of road pavements. To do so, this research developed a number of failure charts that capture engineering knowledge and present possible failure paths, detailing a set of factors contributing to failure. One technique, based on support vector machines, was identified as the most suitable for this research. The developed prototype system, consisting of a failure system for rutting, fatigue cracking, and shear, performed well in both the development phase and network testing of the system. A case study focusing on rural New Zealand roads was carried out, which demonstrated the use of this tool in network and project level applications.

Biodiesel is known to improve the fuel properties of alcohol-diesel blends. However biodiesel is obtained from different feedstock and consequently the composition can be different, with varying fatty acid profiles resulting in different physical and chemical properties and a different response when blended with alcohol-diesel blends. To understand the effect of molecular structure of biodiesel on fuel properties and emissions, the most representative individual fatty acid methyl esters were added to alcohol-diesel blends. The results show that 15% of all methyl esters was enough to avoid phase separation of alcohol-diesel blends and keep the wear scar diameter of the blends below the limitation required by lubricity standards. Short carbon chain length and saturated methyl ester are recommended to improve emissions of alcohol-diesel blends. A comparison between two different alcohols used in the engine tests highlighted that butanol blends were more effective in reducing carbonaceous gas emissions and particulate matter emissions than ethanol blends. Further research on the effect of molecular structure of biodiesel on alcohol-diesel blends was conducted to understand influence of hydroxylated biodiesel which is derived from castor oil. The existence of hydroxyl group in biodiesel considerably improves the lubricity of alcohol-diesel blends. It was also shown to be beneficial in terms of engine-out emissions such as enhancing soot oxidation and reducing activation energy to oxidise soot emissions. To counteract the likely increase in gaseous carbonaceous emissions with alcohol blends, the addition of hydrogen to replace part of the carbon within the liquid fuel was studied. The incorporation of hydrogen and alcohol blends indicates that there was a dramatic reduction in carbon dioxide, unburnt hydrocarbons and particulate matter emissions.

Fine-grained soils, containing clay minerals, are known to experience potentially considerable changes in geotechnical properties when exposed to external loads. These could lead to loss of mechanical performance or catastrophic failure. Therefore, this research focuses on simple geophysical sensing techniques which could contribute to a warning system based on correlations between geotechnical and geophysical parameters. Fine-grained soils of differing plasticity were exposed to changes in vertical loading in bespoke experimental chambers. The geophysical properties investigated were apparent permittivity (AP) and bulk electric conductivity (BEC) in a kHz frequency range, measured with time domain reflectometry (TDR) and bulk electrical conductivity (EC) in a Hz frequency range, measured with electrical resistivity (ER). For what is believed to be the first time, TDR measurements were carried out continuously and in both the vertical and horizontal direction during the volumetric change of the soil, induced by changes in the loading conditions. In parallel, EC was measured, utilising the bespoke test chambers and custom-built acquisition system. Based on the results, it is envisaged that monitoring the relative change in AP and BEC over time, in conjunction with spatially distributed ER arrays, could provide temporal indication of the soil response to load under near-saturated conditions.

While the individual need for dosage of nickel and cobalt in anaerobic digesters has been established, together with the biochemistry underpinning such need; their co-requisite in anaerobic digestion of acetate has not been extensively studied. In addition, the balance between the catalytic and toxic concentrations of nickel and cobalt in anaerobic acetate digesters is not well documented. The aim of this study was to examine and evaluate the effects of individual and combined dosage of nickel and cobalt on biogas production as well as on methanogenic population balance of a mesophilic (37°C) anaerobic acetate digester so as to determine their catalytic and toxic concentrations. Four semi-CSTR digesters were daily fed acetate at a loading rate of 1.8 g L-1 d-1 and were operated at a HRT of 10 days for this investigation. Co-dosage of nickel and cobalt in anaerobic acetate digesters were found to lead to increased biogas production in an additive manner, with the sole dosage of nickel resulting in more biogas production than that of cobalt. At a large enough concentration, these elements were found to inhibit biogas production.