Integrating the finite element method and genetic algorithms to solve structural damage detection and design optimisation problems

This thesis documents fundamental new research in to a specific application of structural box-section beams, for which weight reduction is highly desirable. It is proposed and demonstrated that the weight of these beams can be significantly reduced by using advanced, laminated fibre-reinforced composites in place of steel. Of the many issues raised during this investigation two, of particular importance, are considered in detail; (a) the detection and quantification of damage in composite structures and (b) the optimisation of laminate design to maximise the performance of loaded composite structuress ubject to given constraints. It is demonstrated that both these issues can be formulated and solved as optimisation problems using the finite element method, in which an appropriate objective function is minimised (or maximised). In case (a) the difference in static response obtained from a loaded structure containing damage and an equivalent mathematical model of the structure is minimised by iteratively updating the model. This reveals the damage within the model and subsequently allows the residual properties of the damaged structure to be quantified. Within the scope of this work is the ability to resolve damage, that consists of either penny-shaped sub-surface flaws or tearing damage of box-section beams from surface experimental data. In case (b) an objective function is formulated in terms of a given structural response, or combination of responses that is optimised in order to return an optimal structure, rather than just a satisfactory structure. For the solution of these optimisation problems a novel software tool, based on the integration of genetic algorithms and a commercially available finite element (FE) package, has been developed. A particular advantage of the described method is its applicability to a wide range of engineering problems. The tool is described and its effectiveness demonstrated with reference to two inverse damage detection and quantification problems and one laminate design optimisation problem. The tool allows the full suite of functions within the FE software to be used to solve non-convex optimisation problems, formulated in terms of both discrete and continuous variables, without explicitly stating the form of the stiffness matrix. Furthermore, a priori knowledge about the problem may be readily incorporated in to the method

Contributions on evolutionary computation for statistical inference

Evolutionary Computation (EC) techniques have been introduced in the 1960s for dealing with complex situations. One possible example is an optimization problems not having an analytical solution or being computationally intractable; in many cases such methods, named Evolutionary Algorithms (EAs), have been successfully implemented. In statistics there are many situations where complex problems arise, in particular concerning optimization. A general example is when the statistician needs to select, inside a prohibitively large discrete set, just one element, which could be a model, a partition, an experiment, or such: this would be the case of model selection, cluster analysis or design of experiment. In other situations there could be an intractable function of data, such as a likelihood, which needs to be maximized, as it happens in model parameter estimation. These kind of problems are naturally well suited for EAs, and in the last 20 years a large number of papers has been concerned with applications of EAs in tackling statistical issues. The present dissertation is set in this part of literature, as it reports several implementations of EAs in statistics, although being mainly focused on statistical inference problems. Original results are proposed, as well as overviews and surveys on several topics. EAs are employed and analyzed considering various statistical points of view, showing and confirming their efficiency and flexibility. The first proposal is devoted to parametric estimation problems. When EAs are employed in such analysis a novel form of variability related to their stochastic elements is introduced. We shall analyze both variability due to sampling, associated with selected estimator, and variability due to the EA. This analysis is set in a framework of statistical and computational tradeoff question, crucial in nowadays problems, by introducing cost functions related to both data acquisition and EA iterations. The proposed method will be illustrated by means of model building problem examples. Subsequent chapter is concerned with EAs employed in Markov Chain Monte Carlo (MCMC) sampling. When sampling from multimodal or highly correlated distribution is concerned, in fact, a possible strategy suggests to run several chains in parallel, in order to improve their mixing. If these chains are allowed to interact with each other then many analogies with EC techniques can be observed, and this has led to research in many fields. The chapter aims at reviewing various methods found in literature which conjugates EC techniques and MCMC sampling, in order to identify specific and common procedures, and unifying them in a framework of EC. In the last proposal we present a complex time series model and an identification procedure based on Genetic Algorithms (GAs). The model is capable of dealing with seasonality, by Periodic AutoRegressive (PAR) modelling, and structural changes in time, leading to a nonstationary structure. As far as a very large number of parameters and possibilites of change points are concerned, GAs are appropriate for identifying such model. Effectiveness of procedure is shown on both simulated data and real examples, these latter referred to river flow data in hydrology. The thesis concludes with some final remarks, concerning also future work

BAC transgene arrays as a model system for studying large-scale chromatin structure

The folding of interphase chromatin into large-scale chromatin structure and its spatial organization within nucleus has been suggested to have important roles in gene regulation. In this study, we created engineered chromatin regions consisting of tandem repeats of BAC transgenes, which contain 150-200 kb of defined genomic regions, and used them as a model system to study the mechanisms and functional significance of large-scale chromatin organization. The BAC transgene arrays recapitulated several important features of endogenous chromatin, including transcription level and intranuclear positioning. Using this system, we showed that tandem arrays of housekeeping gene loci form open large-scale chromatin structure independent of their genomic integration sites, including insertions within centromeric heterochromatin. This BAC-specific large-scale chromatin conformation provided a permissive environment for transcription, as evidenced by the copy-number dependent and position independent expression of embedded reporter mini-genes. This leads to the development of a novel method for reliable transgene expression in mammalian cells, which should prove useful in a number of therapeutic and scientific applications. We also demonstrated that BAC transgene arrays can be employed as an effective system for dissecting sequence determinants for intranuclear positioning of gene loci. We showed that in mouse ES and fibroblast cells a BAC carrying a 200 kb human genomic fragment containing the beta-globin locus autonomously targets to the nuclear periphery. Using BAC recombineering, we dissected this 200kb region and identified two genomic regions sufficient to target the BAC transgenes to nuclear periphery. This study represents a first step towards elucidation of the molecular mechanism for the nuclear peripheral localization of genes in mammalian cells

Contributions on evolutionary computation for statistical inference

Evolutionary Computation (EC) techniques have been introduced in the 1960s for dealing with complex situations. One possible example is an optimization problems not having an analytical solution or being computationally intractable; in many cases such methods, named Evolutionary Algorithms (EAs), have been successfully implemented. In statistics there are many situations where complex problems arise, in particular concerning optimization. A general example is when the statistician needs to select, inside a prohibitively large discrete set, just one element, which could be a model, a partition, an experiment, or such: this would be the case of model selection, cluster analysis or design of experiment. In other situations there could be an intractable function of data, such as a likelihood, which needs to be maximized, as it happens in model parameter estimation. These kind of problems are naturally well suited for EAs, and in the last 20 years a large number of papers has been concerned with applications of EAs in tackling statistical issues. The present dissertation is set in this part of literature, as it reports several implementations of EAs in statistics, although being mainly focused on statistical inference problems. Original results are proposed, as well as overviews and surveys on several topics. EAs are employed and analyzed considering various statistical points of view, showing and confirming their efficiency and flexibility. The first proposal is devoted to parametric estimation problems. When EAs are employed in such analysis a novel form of variability related to their stochastic elements is introduced. We shall analyze both variability due to sampling, associated with selected estimator, and variability due to the EA. This analysis is set in a framework of statistical and computational tradeoff question, crucial in nowadays problems, by introducing cost functions related to both data acquisition and EA iterations. The proposed method will be illustrated by means of model building problem examples. Subsequent chapter is concerned with EAs employed in Markov Chain Monte Carlo (MCMC) sampling. When sampling from multimodal or highly correlated distribution is concerned, in fact, a possible strategy suggests to run several chains in parallel, in order to improve their mixing. If these chains are allowed to interact with each other then many analogies with EC techniques can be observed, and this has led to research in many fields. The chapter aims at reviewing various methods found in literature which conjugates EC techniques and MCMC sampling, in order to identify specific and common procedures, and unifying them in a framework of EC. In the last proposal we present a complex time series model and an identification procedure based on Genetic Algorithms (GAs). The model is capable of dealing with seasonality, by Periodic AutoRegressive (PAR) modelling, and structural changes in time, leading to a nonstationary structure. As far as a very large number of parameters and possibilites of change points are concerned, GAs are appropriate for identifying such model. Effectiveness of procedure is shown on both simulated data and real examples, these latter referred to river flow data in hydrology. The thesis concludes with some final remarks, concerning also future work

Advances in Computer Science and Engineering

The book Advances in Computer Science and Engineering constitutes the revised selection of 23 chapters written by scientists and researchers from all over the world. The chapters cover topics in the scientific fields of Applied Computing Techniques, Innovations in Mechanical Engineering, Electrical Engineering and Applications and Advances in Applied Modeling

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Unravelling higher order chromatin organisation through statistical analysis

Recent technological advances underpinned by high throughput sequencing have given new insights into the three-dimensional structure of mammalian genomes. Chromatin conformation assays have been the critical development in this area, particularly the Hi-C method which ascertains genome-wide patterns of intra and inter-chromosomal contacts. However many open questions remain concerning the functional relevance of such higher order structure, the extent to which it varies, and how it relates to other features of the genomic and epigenomic landscape. Current knowledge of nuclear architecture describes a hierarchical organisation ranging from small loops between individual loci, to megabase-sized self-interacting topological domains (TADs), encompassed within large multimegabase chromosome compartments. In parallel with the discovery of these strata, the ENCODE project has generated vast amounts of data through ChIP-seq, RNA-seq and other assays applied to a wide variety of cell types, forming a comprehensive bioinformatics resource. In this work we combine Hi-C datasets describing physical genomic contacts with a large and diverse array of chromatin features derived at a much finer scale in the same mammalian cell types. These features include levels of bound transcription factors, histone modifications and expression data. These data are then integrated in a statistically rigorous way, through a predictive modelling framework from the machine learning field. These studies were extended, within a collaborative project, to encompass a dataset of matched Hi-C and expression data collected over a murine neural differentiation timecourse. We compare higher order chromatin organisation across a variety of human cell types and find pervasive conservation of chromatin organisation at multiple scales. We also identify structurally variable regions between cell types, that are rich in active enhancers and contain loci of known cell-type specific function. We show that broad aspects of higher order chromatin organisation, such as nuclear compartment domains, can be accurately predicted in a variety of human cell types, using models based upon underlying chromatin features. We dissect these quantitative models and find them to be generalisable to novel cell types, presumably reflecting fundamental biological rules linking compartments with key activating and repressive signals. These models describe the strong interconnectedness between locus-level patterns of local histone modifications and bound factors, on the order of hundreds or thousands of basepairs, with much broader compartmentalisation of large, multi-megabase chromosomal regions. Finally, boundary regions are investigated in terms of chromatin features and co-localisation with other known nuclear structures, such as association with the nuclear lamina. We find boundary complexity to vary between cell types and link TAD aggregations to previously described lamina-associated domains, as well as exploring the concept of meta-boundaries that span multiple levels of organisation. Together these analyses lend quantitative evidence to a model of higher order genome organisation that is largely stable between cell types, but can selectively vary locally, based on the activation or repression of key loci

CaTCHing the functional and structural properties of chromosome folding

Proper development requires that genes are expressed at the right time, in the right tissue, and at the right transcriptional level. In metazoans, this involves long-range cis-regulatory elements such as enhancers, which can be located up to hundreds of kilobases away from their target promoters. How enhancers find their target genes and avoid aberrant interactions with non-target genes is currently under intense investigations. The predominant model for enhancer function involves its direct physical looping between the enhancer and target promoter. The three-dimensional organization of chromatin, which accommodates promoter- enhancer interactions, therefore might play an important role in the specificity of these interactions. In the last decade, the development of a class of techniques called chromosome conformation capture (3C) and its derivatives have revolutionized the field of chromatin folding. In particular, the genome-wide version of 3C, Hi-C, revealed that mammalian chromosomes possess a rich hierarchy of folding layers, from multi-megabase compartments corresponding to mutually exclusive associations of active and inactive chromatin to topologically associating domains (TADs), which reflect regions with preferential internal interactions. Although the mechanisms that give rise to this hierarchy are still poorly understood, there is increasing evidence to suggest that TADs represent fundamental functional units for establishing the correct pattern of enhancer-promoter interactions. This is thought to occur through two complementary mechanisms: on the one hand, TADs are thought to increase the chances that regulatory elements meet each other by confining them within the same domain; on the other hand, by segregation of physical interactions across the boundary to avoid unwanted events to occur frequently. It is however unclear whether the properties that have been attributed to TADs are specific to TADs, or rather common features among the whole hierarchy. To address this question, I have implemented an algorithm named Caller of Topological Chromosomal Hierarchies (CaTCH). CaTCH is able to detect nested hierarchies of domains, allowing a comprehensive analysis of structural and functional properties across the folding hierarchy. By applying CaTCH to published Hi-C data in mouse embryonic stem cells (ESCs) and neural progenitor cells (NPCs), I showed that TADs emerge as a functionally privileged scale. In particular, TADs appear to be the scale where accumulation of CTCF at domain boundaries and transcriptional co-regulation during differentiation is maximal. Moreover, TADs appear to be the folding scale where the partitioning of interactions within transcriptionally active domains (and notably between active enhancers and promoters) is optimized. 3C-based methods have enabled fundamental discoveries such as the existence of TADs and CTCF-mediated chromatin loops. 3C methods detect chromatin interactions as ligation products after crosslinking the DNA. Crosslinking and ligation have been often criticized as potential sources of experimental biases, raising the question of whether TADs and CTCF- mediated chromatin loops actually exist in living cells. To address this, in collaboration with Josef Redolfi, we developed a new method termed ‘DamC’ which combines DNA methylation with physical modeling to detect chromosomal interactions in living cells, at the molecular scale, without relying on crosslinking and ligation. By applying DamC to mouse ESCs, we provide the first in vivo and crosslinking- and ligation-free validation of chromosomal structures detected by 3C-methods, namely TADs and CTCF-mediated chromatin loops. DamC, together with 3C-based methods, thus have shown that mammalian chromosomes possess a rich hierarchy of folding layers. An important challenge in the field is to understand the mechanisms that drive the establishment these folding layers. In this sense, polymer physics represent a powerful tool to gain mechanistic insights into the hierarchical folding of mammalian chromosomes. In polymer models, the scaling of contact probability, i.e. the contact probability as a function of genomic distance, has been often used to benchmark polymer simulations and test alternative models. However, the scaling of contact probability is only one of the many properties that characterize polymer models raising the question of whether it would be enough to discriminate alternative polymer models. To address this, I have built finite-size heteropolymer models characterized by random interactions. I showed that finite-size effects, together with the heterogeneity of the interactions, are sufficient to reproduce the observed range of scaling of contact probability. This suggests that one should be careful in discriminating polymer models of chromatin folding based solely on the scaling. In conclusion, my findings have contributed to achieve a better understanding of chromatin folding, which is essential to really understand how enhancers act on promoters. The comprehensive analyses using CaTCH have provided conceptually new insights into how the architectural functionality of TADs may be established. My work on heteropolymer models has highlighted the fact that one should be careful in using solely scaling to discriminate physical models for chromatin folding. Finally, the ability to detect TADs and chromatin loops using DamC represents a fundamental result since it provides the first orthogonal in vivo validation of chromosomal structures that had essentially relied on a single technology