Foundations of population-based SHM, part II : heterogeneous populations – graphs, networks, and communities

This paper is the second in a series of three which aims to provide a basis for Population-Based Structural Health Monitoring (PBSHM); a new technology which will allow transfer of diagnostic information across a population of structures, augmenting SHM capability beyond that applicable to individual structures. The new PBSHM can potentially allow knowledge about normal operating conditions, damage states, and even physics-based models to be transferred between structures. The first part in this series considered homogeneous populations of nominally-identical structures. The theory is extended in this paper to heterogeneous populations of disparate structures. In order to achieve this aim, the paper introduces an abstract representation of structures based on Irreducible Element (IE) models, which capture essential structural characteristics, which are then converted into Attributed Graphs (AGs). The AGs form a complex network of structure models, on which a metric can be used to assess structural similarity; the similarity being a key measure of whether diagnostic information can be successfully transferred. Once a pairwise similarity metric has been established on the network of structures, similar structures are clustered to form communities. Within these communities, it is assumed that a certain level of knowledge transfer is possible. The transfer itself will be accomplished using machine learning methods which will be discussed in the third part of this series. The ideas introduced in this paper can be used to define precise terminology for PBSHM in both the homogeneous and heterogeneous population cases

Normalising flows and nonlinear normal modes

In the context of dynamic decoupling problems, engineering dynamics has long held modal analysis as an exemplar. The method allows the exact decomposition of linear multi-degree-of-freedom (MDOF) systems into single-degree-of-freedom (SDOF) oscillators, thus simplifying complex dynamic problems considerably. However, modal analysis is very much a linear theory; if applied to nonlinear systems, the decoupling property (among others) is lost. This unfortunate situation has led to numerous attempts to formulate workable nonlinear versions of the theory. The current paper extends previous work by the authors in using machine learning methods to learn nonlinear modal transformations on measured data, based on the premise that any latent modal variables should be statistically independent. Unlike previous work, the transformation here exploits the recent development of normalising flows in constructing the required transformations. The new approach is shown to overcome a number of the problems in the original approach when demonstrated on a simulated nonlinear system

Towards population-based structural health monitoring, Part II : heterogeneous populations and structures as graphs

Information about the expected variation in the normal condition and various damage states of a structure is crucial in structural health monitoring. In an ideal case, the behaviour associated with each possible type of damage would be known and classification would be possible. However, it is not realistic to obtain data for every possible damage state in an individual structure. Examining a population of structures gives a much larger pool of data to work with. Machine learning can then potentially allow inferences across the population using algorithms from transfer learning. The degree of similarity between structures determines the level of possible knowledge transfer between different structures. It is also useful to quantify in which ways two structures are similar, and where these similarities lie. This information determines whether or not certain the transfer learning approaches are applicable in a given situation. It is therefore necessary to develop a method for analysing the similarities between structures. First, it must be decided which properties of the structure to use when measuring the similarity. For example, comparing 3D CAD models or Finite Element models is not a suitable approach, since these contain a lot of irrelevant information. It is better to abstract this information into a form that contains only the relevant information. This paper proposes Irreducible Element (IE) models, which are designed to capture the features that are crucial in determining whether or not transfer learning is possible. This information is then converted into an Attributed Graph (AG). The Attributed Graph for a structure contains the same information as the Irreducible Element model; however, the graph carries this information as a list of attributes attached to nodes. Organising the information in this manner makes it easier for graph-matching algorithms to perform a comparison between two structures. This comparison can then be used to generate a measure of similarity between the two structures and determine the most appropriate transfer learning method

Foundations of population-based SHM, Part I : homogeneous populations and forms

In Structural Health Monitoring (SHM), measured data that correspond to an extensive set of operational and damage conditions (for a given structure) are rarely available. One potential solution considers that information might be transferred, in some sense, between similar systems. A population-based approach to SHM looks to both model and transfer this missing information, by considering data collected from groups of similar structures. Specifically, in this work, a framework is proposed to model a population of nominally-identical systems, such that (complete) datasets are only available from a subset of members. The SHM strategy defines a general model, referred to as the population form, which is used to monitor a homogeneous group of systems. First, the framework is demonstrated through applications to a simulated population, with one experimental (test-rig) member; the form is then adapted and applied to signals recorded from an operational wind farm

Sequential bottlenecks drive viral evolution in early acute Hepatitis C virus infection

Hepatitis C is a pandemic human RNA virus, which commonly causes chronic infection and liver disease. The characterization of viral populations that successfully initiate infection, and also those that drive progression to chronicity is instrumental for understanding pathogenesis and vaccine design. A comprehensive and longitudinal analysis of the viral population was conducted in four subjects followed from very early acute infection to resolution of disease outcome. By means of next generation sequencing (NGS) and standard cloning/Sanger sequencing, genetic diversity and viral variants were quantified over the course of the infection at frequencies as low as 0.1%. Phylogenetic analysis of reassembled viral variants revealed acute infection was dominated by two sequential bottleneck events, irrespective of subsequent chronicity or clearance. The first bottleneck was associated with transmission, with one to two viral variants successfully establishing infection. The second occurred approximately 100 days post-infection, and was characterized by a decline in viral diversity. In the two subjects who developed chronic infection, this second bottleneck was followed by the emergence of a new viral population, which evolved from the founder variants via a selective sweep with fixation in a small number of mutated sites. The diversity at sites with non-synonymous mutation was higher in predicted cytotoxic T cell epitopes, suggesting immune-driven evolution. These results provide the first detailed analysis of early within-host evolution of HCV, indicating strong selective forces limit viral evolution in the acute phase of infection

The role of alkalinity generation in controlling the fluxes of CO₂ during exposure and inundation on tidal flats

Dissolved inorganic carbon (DIC), gaseous CO2 and alkalinity fluxes from intertidal sediments were investigated during periods of exposure and inundation, using laboratory core incubations, previously published field data and reactive transport model simulations. In the incubations and previous field data, it was found that during periods of alkalinity production (attributed to the accumulation of reduced sulfur species within the sediment), the flux of DIC out of the sediment was greater during inundation than the gaseous CO2 flux during exposure by a factor of up to 1.8. This finding was supported by computational simulations which indicated that large amounts of sulfate reduction and reduced sulfur burial (FeS) induce an alkalinity flux from the sediment during high tide conditions. Model simulations also found that the amount of reactive Fe in the sediment was a major driver of net alkalinity production. Our finding that CO2 fluxes can be significantly lower than total metabolism during exposure has implications for how total metabolism is quantified on tidal flats

New results from the NA57 experiment

We report results from the experiment NA57 at CERN SPS on hyperon production at midrapidity in Pb-Pb collisions at 158 $A$ GeV/$c$ and 40 $A$ GeV/$c$. $\Lambda$, $\Xi$ and $\Omega$ yields are compared with those from the STAR experiment at the higher energy of the BNL RHIC. $\Lambda$, $\Xi$, $\Omega$\ and preliminary $K_S^0$ transverse mass spectra are presented and interpreted within the framework of a hydro-dynamical blast wave model.Comment: 8 pages, 3 figures, contribution to the proceedings of The XXXVIIIth Rencontres de Moriond "QCD and High Energy Hadronic Interactions

Results on hyperon production from the NA57 experiment

Recent results on hyperon production in Pb--Pb collisions from the NA57 experiment are reported. Strangeness enhancement and the transverse mass spectra properties at 158 GeV per nucleon are described.Comment: submitted to Acta Phys. Hung. A (Heavy Ion Physics

Strange particle production in 158 and 40 AA GeV/cc Pb-Pb and p-Be collisions

Results on strange particle production in Pb-Pb collisions at 158 and 40 $A$ GeV/$c$ beam momentum from the NA57 experiment at CERN SPS are presented. Particle yields and ratios are compared with those measured at RHIC. Strangeness enhancements with respect to p-Be reactions at the same beam momenta have been also measured: results about their dependence on centrality and collision energy are reported and discussed.Comment: Contribution to the proceedings of the "Hot Quarks 2004" Conference, July 18-24 2004, New Mexico, USA, submitted to Journal of Physics G 7 pages, 5 figure