Numerical modelling of braided fibres for reinforced concrete

Fire has been always a major concern for designers of steel and concrete structures. Designing fire-resistant structural elements is not an easy task due to several limitations such as the lack of fire-resistant construction materials. Concrete reinforcement cover and external insulation are the most commonly adopted systems to protect concrete and steel from overheating, while spalling of concrete is minimised by using HPFRC instead of standard concrete. Although these methodologies work very well for low rise concrete structures, this is not the case for high-rise and inaccessible buildings where fire loading is much longer. Fire can permanently damage structures that cost a lot of money. This is unsafe and can lead to loss of life. In this research, the author proposes a new type of main reinforcement for concrete structures which can provide better fire-resistance than steel or FRP re-bars. This consists of continuous braided fibre rope, generally made from fire-resistant materials such as carbon or glass fibre. These fibres have excellent tensile strengths, sometimes in excess of ten times greater than steel. In addition to fire-resistance, these ropes can produce lighter and corrosive resistant structures. Avoiding the use of expensive resin binders, fibres are easily bound together using braiding techniques, ensuring that tensile stress is evenly distributed throughout the reinforcement. In order to consider braided ropes as a form of reinforcement it is first necessary to establish the mechanical performance at room temperature and investigate the pull-out resistance for both unribbed and ribbed ropes. Ribbing of ropes was achieved by braiding the rope over a series of glass beads. Adhesion between the rope and concrete was drastically improved due to ribbing, and further improved by pre-stressing ropes and reducing the slacked fibres. Two types of material have been considered for the ropes: carbon and aramid. An implicit finite element approach is proposed to model braided fibres using Total Lagrangian formulation, based on the theory of small strains and large rotations. Modelling tows and strands as elastic transversely isotropic materials was a good assumption when stiff and brittle fibres such as carbon and glass fibres are considered. The rope-to-concrete and strand-to-strand bond interaction/adhesion was numerically simulated using newly proposed hierarchical higher order interface elements. Elastic and linear damage cohesive models were used effectively to simulate non-penetrative 'free' sliding interaction between strands, and the adhesion between ropes and concrete respectively. Numerical simulation showed similar de-bonding features when compared with experimental pull-out results of braided ribbed rope reinforced concrete.

Sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution

Understanding how virus strains offer protection against closely related emerging strains is vital for creating effective vaccines. For many viruses, including Foot-and-Mouth Disease Virus (FMDV) and the Influenza virus where multiple serotypes often co-circulate, in vitro testing of large numbers of vaccines can be infeasible. Therefore the development of an in silico predictor of cross-protection between strains is important to help optimise vaccine choice. Vaccines will offer cross-protection against closely related strains, but not against those that are antigenically distinct. To be able to predict cross-protection we must understand the antigenic variability within a virus serotype, distinct lineages of a virus, and identify the antigenic residues and evolutionary changes that cause the variability. In this thesis we present a family of sparse hierarchical Bayesian models for detecting relevant antigenic sites in virus evolution (SABRE), as well as an extended version of the method, the extended SABRE (eSABRE) method, which better takes into account the data collection process. The SABRE methods are a family of sparse Bayesian hierarchical models that use spike and slab priors to identify sites in the viral protein which are important for the neutralisation of the virus. In this thesis we demonstrate how the SABRE methods can be used to identify antigenic residues within different serotypes and show how the SABRE method outperforms established methods, mixed-effects models based on forward variable selection or l1 regularisation, on both synthetic and viral datasets. In addition we also test a number of different versions of the SABRE method, compare conjugate and semi-conjugate prior specifications and an alternative to the spike and slab prior; the binary mask model. We also propose novel proposal mechanisms for the Markov chain Monte Carlo (MCMC) simulations, which improve mixing and convergence over that of the established component-wise Gibbs sampler. The SABRE method is then applied to datasets from FMDV and the Influenza virus in order to identify a number of known antigenic residue and to provide hypotheses of other potentially antigenic residues. We also demonstrate how the SABRE methods can be used to create accurate predictions of the important evolutionary changes of the FMDV serotypes. In this thesis we provide an extended version of the SABRE method, the eSABRE method, based on a latent variable model. The eSABRE method takes further into account the structure of the datasets for FMDV and the Influenza virus through the latent variable model and gives an improvement in the modelling of the error. We show how the eSABRE method outperforms the SABRE methods in simulation studies and propose a new information criterion for selecting the random effects factors that should be included in the eSABRE method; block integrated Widely Applicable Information Criterion (biWAIC). We demonstrate how biWAIC performs equally to two other methods for selecting the random effects factors and combine it with the eSABRE method to apply it to two large Influenza datasets. Inference in these large datasets is computationally infeasible with the SABRE methods, but as a result of the improved structure of the likelihood, we are able to show how the eSABRE method offers a computational improvement, leading it to be used on these datasets. The results of the eSABRE method show that we can use the method in a fully automatic manner to identify a large number of antigenic residues on a variety of the antigenic sites of two Influenza serotypes, as well as making predictions of a number of nearby sites that may also be antigenic and are worthy of further experiment investigation.