Exploiting the environment for structural health monitoring: Strategies, algorithms, and technologies

A densely built environment is a complex system of infrastructure, nature, and people closely interconnected and interacting. Vehicles, public transport, weather action, and sports activities constitute a manifold set of excitation and degradation sources for civil structures. In this context, operators should consider different factors in a holistic approach for assessing the structural health state. Vibration-based structural health monitoring (SHM) has demonstrated great potential as a decision-supporting tool to schedule maintenance interventions. However, most excitation sources are considered an issue for practical SHM applications since traditional methods are typically based on strict assumptions on input stationarity. Last-generation low-cost sensors present limitations related to a modest sensitivity and high noise floor compared to traditional instrumentation. If these devices are used for SHM in urban scenarios, short vibration recordings collected during high-intensity events and vehicle passage may be the only available datasets with a sufficient signal-to-noise ratio. While researchers have spent efforts to mitigate the effects of short-term phenomena in vibration-based SHM, the ultimate goal of this thesis is to exploit them and obtain valuable information on the structural health state. First, this thesis proposes strategies and algorithms for smart sensors operating individually or in a distributed computing framework to identify damage-sensitive features based on instantaneous modal parameters and influence lines. Ordinary traffic and people activities become essential sources of excitation, while human-powered vehicles, instrumented with smartphones, take the role of roving sensors in crowdsourced monitoring strategies. The technical and computational apparatus is optimized using in-memory computing technologies. Moreover, identifying additional local features can be particularly useful to support the damage assessment of complex structures. Thereby, smart coatings are studied to enable the self-sensing properties of ordinary structural elements. In this context, a machine-learning-aided tomography method is proposed to interpret the data provided by a nanocomposite paint interrogated electrically.

Computational design of a new type of sandwich cross-section for a defined solution of a tree-like column produced by wire-and-arc additive manufacturing

When it comes to designing a structure, architects and engineers want to join forces in order to create and build the most beautiful and efficient building. From finding new shapes and forms to optimizing the stability and the resistance, there is a constant link to be made between both professions. In architecture, there has always been a particular interest in creating new shapes and types of a structure inspired by many different fields, one of them being nature itself. In engineering, the selection of optimum has always dictated the way of thinking and designing structures. This mindset led through studies to the current best practices in construction. However, both disciplines were limited by the traditional manufacturing constraints at a certain point. Over the last decades, much progress was made from a technological point of view, allowing to go beyond today's manufacturing constraints. With the emergence of Wire-and-Arc Additive Manufacturing (WAAM) combined with Algorithmic-Aided Design (AAD), architects and engineers are offered new opportunities to merge architectural beauty and structural efficiency. Both technologies allow for exploring and building unusual and complex structural shapes in addition to a reduction of costs and environmental impacts. Through this study, the author wants to make use of previously mentioned technologies and assess their potential, first to design an aesthetically appreciated tree-like column with the idea of secondly proposing a new type of standardized and optimized sandwich cross-section to the construction industry. Parametric algorithms to model the dendriform column and the new sandwich cross-section are developed and presented in detail. A catalog draft of the latter and methods to establish it are then proposed and discussed. Finally, the buckling behavior of this latter is assessed considering standard steel and WAAM material properties.

Biased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.

Abstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.

A Rosetta Stone For Nature's Benefits To People.

After a long incubation period, the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) is now underway. Underpinning all its activities is the IPBES Conceptual Framework (CF), a simplified model of the interactions between nature and people. Drawing on the legacy of previous large-scale environmental assessments, the CF goes further in explicitly embracing different disciplines and knowledge systems (including indigenous and local knowledge) in the co-construction of assessments of the state of the world's biodiversity and the benefits it provides to humans. The CF can be thought of as a kind of Rosetta Stone that highlights commonalities between diverse value sets and seeks to facilitate crossdisciplinary and crosscultural understanding. We argue that the CF will contribute to the increasing trend towards interdisciplinarity in understanding and managing the environment. Rather than displacing disciplinary science, however, we believe that the CF will provide new contexts of discovery and policy applications for it.

Pre-processing for noise detection in gene expression classification data

Due to the imprecise nature of biological experiments, biological data is often characterized by the presence of redundant and noisy data. This may be due to errors that occurred during data collection, such as contaminations in laboratorial samples. It is the case of gene expression data, where the equipments and tools currently used frequently produce noisy biological data. Machine Learning algorithms have been successfully used in gene expression data analysis. Although many Machine Learning algorithms can deal with noise, detecting and removing noisy instances from the training data set can help the induction of the target hypothesis. This paper evaluates the use of distance-based pre-processing techniques for noise detection in gene expression data classification problems. This evaluation analyzes the effectiveness of the techniques investigated in removing noisy data, measured by the accuracy obtained by different Machine Learning classifiers over the pre-processed data.

The non-equilibrium nature of culinary evolution

Food is an essential part of civilization, with a scope that ranges from the biological to the economic and cultural levels. Here, we study the statistics of ingredients and recipes taken from Brazilian, British, French and Medieval cookery books. We find universal distributions with scale invariant behaviour. We propose a copy-mutate process to model culinary evolution that fits our empirical data very well. We find a cultural 'founder effect' produced by the non-equilibrium dynamics of the model. Both the invariant and idiosyncratic aspects of culture are accounted for by our model, which may have applications in other kinds of evolutionary processes.

Chemical potential and the nature of dark energy: The case of a phantom field

The influence of a possible nonzero chemical potential mu on the nature of dark energy is investigated by assuming that the dark energy is a relativistic perfect simple fluid obeying the equation of state, p=omega rho (omega < 0, constant). The entropy condition, S >= 0, implies that the possible values of omega are heavily dependent on the magnitude, as well as on the sign of the chemical potential. For mu > 0, the omega parameter must be greater than -1 (vacuum is forbidden) while for mu < 0 not only the vacuum but even a phantomlike behavior (omega <-1) is allowed. In any case, the ratio between the chemical potential and temperature remains constant, that is, mu/T=mu(0)/T(0). Assuming that the dark energy constituents have either a bosonic or fermionic nature, the general form of the spectrum is also proposed. For bosons mu is always negative and the extended Wien's law allows only a dark component with omega <-1/2, which includes vacuum and the phantomlike cases. The same happens in the fermionic branch for mu < 0. However, fermionic particles with mu > 0 are permitted only if -1

Distinguishing post-AGB impostors in a sample of pre-main sequence stars

Context. A sample of 27 sources, cataloged as pre-main sequence stars by the Pico dos Dias Survey (PDS), is analyzed to investigate a possible contamination by post-AGB stars. The far-infrared excess due to dust present in the circumstellar envelope is typical of both categories: young stars and objects that have already left the main sequence and are suffering severe mass loss. Aims. The two known post-AGB stars in our sample inspired us to seek for other very likely or possible post-AGB objects among PDS sources previously suggested to be Herbig Ae/Be stars, by revisiting the observational database of this sample. Methods. In a comparative study with well known post-AGBs, several characteristics were evaluated: (i) parameters related to the circumstellar emission; (ii) spatial distribution to verify the background contribution from dark clouds; (iii) spectral features; and (iv) optical and infrared colors. Results. These characteristics suggest that seven objects of the studied sample are very likely post-AGBs, five are possible post-AGBs, eight are unlikely post-AGBs, and the nature of seven objects remains unclear.

SPITZER OBSERVATIONS OF A1763. II. CONSTRAINING THE NATURE OF ACTIVITY IN THE CLUSTER-FEEDING FILAMENT WITH VLA AND XMM-NEWTON DATA

The A1763 superstructure at z = 0.23 contains the first galaxy filament to be directly detected using mid-infrared observations. Our previous work has shown that the frequency of starbursting galaxies, as characterized by 24 mu m emission is much higher within the filament than at either the center of the rich galaxy cluster, or the field surrounding the system. New Very Large Array and XMM-Newton data are presented here. We use the radio and X-ray data to examine the fraction and location of active galaxies, both active galactic nuclei (AGNs) and starbursts (SBs). The radio far-infrared correlation, X-ray point source location, IRAC colors, and quasar positions are all used to gain an understanding of the presence of dominant AGNs. We find very few MIPS-selected galaxies that are clearly dominated by AGN activity. Most radio-selected members within the filament are SBs. Within the supercluster, three of eight spectroscopic members detected both in the radio and in the mid-infrared are radio-bright AGNs. They are found at or near the core of A1763. The five SBs are located further along the filament. We calculate the physical properties of the known wide angle tail (WAT) source which is the brightest cluster galaxy of A1763. A second double lobe source is found along the filament well outside of the virial radius of either cluster. The velocity offset of the WAT from the X-ray centroid and the bend of the WAT in the intracluster medium are both consistent with ram pressure stripping, indicative of streaming motions along the direction of the filament. We consider this as further evidence of the cluster-feeding nature of the galaxy filament.

Nature of the X(3872) from QCD

We have studied some possible four-quark and molecule configurations of the X(3872) using double ratios of sum rules, which are more accurate than the usual simple ratios often used in the literature to obtain hadron masses. We found that the different structures ((3) over bar - (3) over bar and (6) over bar - 6 tetraquarks and D - D(*) molecule) lead to the same prediction for the mass (within the accuracy of the method), indicating that the alone prediction of the X mass may not be sufficient to reveal its nature. In doing these analyses, we also find that (within our approximation) the use of the (MS) over bar running (m) over bar (c)(m(c)(2)), rather than the on-shell mass, is more appropriate to obtain the J/psi and X meson masses. Using vertex sum rules to roughly estimate the X(3872) hadronic and radiative widths, we found that the available experimental data does not exclude a lambda - J/psi-like molecule current.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

An algorithmic Friedman-Pippenger theorem on tree embeddings and applications

An (n, d)-expander is a graph G = (V, E) such that for every X subset of V with vertical bar X vertical bar <= 2n - 2 we have vertical bar Gamma(G)(X) vertical bar >= (d + 1) vertical bar X vertical bar. A tree T is small if it has at most n vertices and has maximum degree at most d. Friedman and Pippenger (1987) proved that any ( n; d)- expander contains every small tree. However, their elegant proof does not seem to yield an efficient algorithm for obtaining the tree. In this paper, we give an alternative result that does admit a polynomial time algorithm for finding the immersion of any small tree in subgraphs G of (N, D, lambda)-graphs Lambda, as long as G contains a positive fraction of the edges of Lambda and lambda/D is small enough. In several applications of the Friedman-Pippenger theorem, including the ones in the original paper of those authors, the (n, d)-expander G is a subgraph of an (N, D, lambda)-graph as above. Therefore, our result suffices to provide efficient algorithms for such previously non-constructive applications. As an example, we discuss a recent result of Alon, Krivelevich, and Sudakov (2007) concerning embedding nearly spanning bounded degree trees, the proof of which makes use of the Friedman-Pippenger theorem. We shall also show a construction inspired on Wigderson-Zuckerman expander graphs for which any sufficiently dense subgraph contains all trees of sizes and maximum degrees achieving essentially optimal parameters. Our algorithmic approach is based on a reduction of the tree embedding problem to a certain on-line matching problem for bipartite graphs, solved by Aggarwal et al. (1996).

The enzymatic nature of fungal bioluminescence

The uncertainty about the possible involvement of a luciferase in fungal bioluminescence has not only hindered the understanding of its biochemistry but also delayed the characterization of its constituents. The present report describes how in vitro light emission can be obtained enzymatically from the cold and hot extracts assay using different species of fungi, which also indicates a common mechanism for all these organisms. Kinetic data suggest a consecutive two-step enzymatic mechanism and corroborate the enzymatic proposal of Airth and Foerster. Finally, overlapping of light emission spectra from the fungal bioluminescence and the in vitro assay confirm that this reaction is the same one that occurs in live fungi.

H(infinity) filtering for rectangular discrete-time descriptor systems

This paper deals with the H(infinity) recursive estimation problem for general rectangular time-variant descriptor systems in discrete time. Riccati-equation based recursions for filtered and predicted estimates are developed based on a data fitting approach and game theory. In this approach, the nature determines a state sequence seeking to maximize the estimation cost, whereas the estimator tries to find an estimate that brings the estimation cost to a minimum. A solution exists for a specified gamma-level if the resulting cost is positive. In order to present some computational alternatives to the H(infinity) filters developed, they are rewritten in information form along with the respective array algorithms. (C) 2009 Elsevier Ltd. All rights reserved.

Treatment of industrial effluents of recalcitrant nature using ozone, hydrogen peroxide and ultraviolet radiation

Currently diverse industries have high pollution potential because their productive processes generate great volumes of refractory effluents. These effluents are problematic, mainly due to the presence of recalcitrant compounds that are detrimental in wastewater treatment plants using biological systems in their processes. In general, biological treatments do not remove refractory elements. Also, in most cases these compounds can inhibit the yield or are toxic for biota responsible to remove the polluting agents. The Advanced Oxidative Processes (AOPs) represent a technological alternative with a great potential for treatment of no biodegradable effluents. In this paper a review of the use of advanced oxidatives processes: Ozone (O(3)), peroxide of hydrogen (H(2)O(2)) and ultraviolet radiation (UV) is presented applied to the treatment of recalcitrant effluents.