Asphaltenes constitute the heaviest, most polar and aromatic fraction of petroleum crucial to the formation of highly-stable water-in-crude oil emulsions. The latter occur during crude oil production as well as spills and cause difficulties to efficient remediation practice. It is thought that in nanoaggregate form, asphaltenes create elastic layers around water droplets enhancing stability of the emulsion matrix. Ultrasonic characterisation is a high-resolution non-invasive tool in colloidal analysis shown to successfully identify asphaltene nanoaggregation in toluene. The high sensitivity of acoustic velocity to molecular rearrangements and ease in implementation renders it an attractive method to study asphaltene phase properties. Currently, aggregation is thought to correspond to an intersection of two concentration-ultrasonic velocity regressions. Our measurements indicate a variation in the proximity of nanoaggregation which is not accounted for by present models. We attribute this uncertainty to physico-chemical heterogeneity of the asphaltene fraction driven by variation in molecular size and propose a critical nanoaggregation region. We treated asphaltenes from North and South American crude oils with ruthenium ion catalysed oxidation to characterize their n-alkyl appendages attached to aromatic cores. Principal component analysis was performed to investigate the coupling between asphaltene structures and velocity measurements and their impact on aggregation.

We propose using fully Bayesian Gaussian process emulation (GPE) as a surrogate for expensive computer experiments of transport infrastructure cut slopes in high-plasticity clay soils that are associated with an increased risk of failure. Our deterioration experiments simulate the dissipation of excess pore water pressure and seasonal pore water pressure cycles to determine slope failure time. It is impractical to perform the number of computer simulations that would be sufficient to make slope stability predictions over a meaningful range of geometries and strength parameters. Therefore, a GPE is used as an interpolator over a set of optimally spaced simulator runs modeling the time to slope failure as a function of geometry, strength, and permeability. Bayesian inference and Markov chain Monte Carlo simulation are used to obtain posterior estimates of the GPE parameters. For the experiments that do not reach failure within model time of 184 years, the time to failure is stochastically imputed by the Bayesian model. The trained GPE has the potential to inform infrastructure slope design, management, and maintenance. The reduction in computational cost compared with the original simulator makes it a highly attractive tool which can be applied to the different spatio-temporal scales of transport networks.

Abstract. The West Antarctic Ice Sheet (WAIS) is one of the largest potential sources of future sea-level rise, with glaciers draining the WAIS thinning at an accelerating rate over the past 40 years. Due to complexities in calibrating palaeoceanographic proxies for the Southern Ocean, it remains difficult to assess whether similar changes have occurred earlier during the Holocene or whether there is underlying centennial- to millennial-scale forcing in oceanic variability. Archaeal lipid-based proxies, specifically glycerol dialkyl glycerol tetraether (GDGT; e.g. TEX86 and TEX86L), are powerful tools for reconstructing ocean temperature, but these proxies have been shown previously to be difficult to apply to the Southern Ocean. A greater understanding of the parameters that control Southern Ocean GDGT distributions would improve the application of these biomarker proxies and thus help provide a longer-term perspective on ocean forcing of Antarctic ice sheet changes. In this study, we characterised intact polar lipid (IPL)-GDGTs, representing (recently) living archaeal populations in suspended particulate matter (SPM) from the Amundsen Sea and the Scotia Sea. SPM samples from the Amundsen Sea were collected from up to four water column depths representing the surface waters through to Circumpolar Deep Water (CDW), whereas the Scotia Sea samples were collected along a transect encompassing the sub-Antarctic front through to the southern boundary of the Antarctic Circumpolar Current. IPL-GDGTs with low cyclic diversity were detected throughout the water column with high relative abundances of hydroxylated IPL-GDGTs identified in both the Amundsen and Scotia seas. Results from the Scotia Sea show shifts in IPL-GDGT signatures across well-defined fronts of the Southern Ocean. Indicating that the physicochemical parameters of these water masses determine changes in IPL-GDGT distributions. The Amundsen Sea results identified GDGTs with hexose-phosphohexose head groups in the CDW, suggesting active GDGT synthesis at these depths. These results suggest that GDGTs synthesised at CDW depths may be a significant source of GDGTs exported to the sedimentary record and that temperature reconstructions based on TEX86 or TEX86L proxies may be significantly influenced by the warmer waters of the CDW.

Bayesian inference and ultrasonic velocity have been used to estimate the self-association concentration of the asphaltenes in toluene using a changepoint regression model. The estimated values agree with the literature information and indicate that a lower abundance of the longer side-chains can cause an earlier onset of asphaltene self-association. Asphaltenes constitute the heaviest and most complicated fraction of crude petroleum and include a surface-active sub-fraction. When present above a critical concentration in pure solvent, asphaltene “monomers” self-associate and form nanoaggregates. Asphaltene nanoaggregates are thought to play a significant role during the remediation of petroleum spills and seeps. When mixed with water, petroleum becomes expensive to remove from the water column by conventional methods. The main reason of this difficulty is the presence of highly surface-active asphaltenes in petroleum. The nanoaggregates are thought to surround the water droplets, making the water-in-oil emulsions extremely stable. Due to their molecular complexity, modelling the self-association of the asphaltenes can be a very computationally-intensive task and has mostly been approached by molecular dynamic simulations. Our approach allows the use of literature and experimental data to estimate the nanoaggregation and its credible intervals. It has a low computational cost and can also be used for other analytical/experimental methods probing a changepoint in the molecular association behaviour.

Earthwork assets, including cut slopes and embankments, are essential components of the infrastructure supporting road and rail transportation networks. Asset owners must assess the stability of these slopes as they deteriorate, to prevent unwanted slope failures. Assessing the stability of individual earthworks within a portfolio using slope stability analyses can be expensive and time-consuming. Hence, a Bayesian logistic regression model was developed to evaluate the probability of slope failure, using training data from published case histories of slope failures. The Bayesian model was then used to assess the probability of failure for the more specific case of clay cut slopes within a railway earthwork asset portfolio owned by Network Rail (NR). The portfolio includes earthworks at various stages of degraded strength and with different drainage conditions. The results from models with material properties that were equivalent to those for the deteriorated strength of clays compared most closely with clay cut slope failures within the NR dataset. Steeper slopes (>35 degrees) had the highest probability of failure, regardless of the drainage condition. However, for shallower slopes, the poorly-drained slopes had a ≈20% higher probability of failure than the well-drained slopes.