Avaliação da integridade estrutural de elementos de concreto armado a partir das propriedades modais obtidas por técnicas de excitações aleatórias e transientes

A análise dinâmica experimental tem sido amplamente pesquisada como uma ferramenta de avaliação de integridade de estruturas de concreto armado. Existem técnicas de identificação de danos baseadas em propriedades modais como frequências de ressonâncias, deformadas modais, curvaturas modais e amortecimento. Há também técnicas baseadas na não linearidade da resposta dinâmica, que apesar do grande potencial na detecção de danos, têm sido pouco exploradas nos últimos anos. Este trabalho tem por objetivo avaliar a integridade estrutural de vigas de concreto armado através do comportamento da resposta dinâmica. Foram realizados ensaios dinâmicos em duas vigas de concreto armado com 3,5 m de comprimento, 25 cm de largura, 35 cm de altura e idênticas taxas de armaduras, mas configuradas com barras de aço de diferentes diâmetros, 2 ϕ 16 mm e 8 ϕ 8 mm, respectivamente. Tais vigas, inicialmente íntegras, foram submetidas a ciclos de carregamento e descarregamento com intensidades crescentes até atingir a ruptura do elemento. Após cada ciclo, as propriedades dinâmicas foram avaliadas experimentalmente, com o emprego de técnicas de excitação por sinais do tipo aleatório e tipo transiente, respectivamente, visando determinar parâmetros que indiquem a deterioração gradativa do elemento. Nesses ensaios dinâmicos aplicaram-se diferentes amplitudes da força de excitação. Verificou-se que o aumento da amplitude da força dinâmica de excitação provocou reduções nos valores das frequências de ressonância de 1,1% e 2,4%, associadas, respectivamente, às excitações aleatórias e transientes; e um comportamento não linear dos índices de amortecimento, associados às excitações aleatórias, mantendo um crescimento linear com as excitações transientes. Constatou-se, ainda, que os valores das frequências de ressonância decrescem com a redução de rigidez mecânica, diminuída com o aumento do nível de fissuração induzido nos modelos. Já os valores dos índices de amortecimento, após cada ciclo, se comportaram de forma não linear e assumiram diferentes valores, conforme a técnica de excitação empregada. Acredita-se que esta não linearidade está relacionada aos danos provocados no elemento pela solicitação estrutural e, por consequência, ao processo de como a dissipação de energia é empregada no processo de instauração, configuração e propagação das fissuras nos elementos de concreto armado.

Enhanced detection-guided NLMS estimation of sparse FIR-modeled signal channels

In various signal-channel-estimation problems, the channel being estimated may be well approximated by a discrete finite impulse response (FIR) model with sparsely separated active or nonzero taps. A common approach to estimating such channels involves a discrete normalized least-mean-square (NLMS) adaptive FIR filter, every tap of which is adapted at each sample interval. Such an approach suffers from slow convergence rates and poor tracking when the required FIR filter is "long." Recently, NLMS-based algorithms have been proposed that employ least-squares-based structural detection techniques to exploit possible sparse channel structure and subsequently provide improved estimation performance. However, these algorithms perform poorly when there is a large dynamic range amongst the active taps. In this paper, we propose two modifications to the previous algorithms, which essentially remove this limitation. The modifications also significantly improve the applicability of the detection technique to structurally time varying channels. Importantly, for sparse channels, the computational cost of the newly proposed detection-guided NLMS estimator is only marginally greater than that of the standard NLMS estimator. Simulations demonstrate the favourable performance of the newly proposed algorithm. © 2006 IEEE.

Synthesis of azulenylsilane nitrones as diagnostic tools for superoxide detection

The superoxide radical is considered to play important roles in physiological processes as well as in the genesis of diverse cytotoxic conditions such as cancer, various cardiovascular disorders and neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD) and Alzheimer’s disease (AD). The detection and quantification of superoxide within cells is of critical importance to understand biological roles of superoxide and to develop preventive strategies against free radical-mediated diseases. Cyclic nitrone spin traps such as DMPO, EMPO, DEPMPO, BMPO and their derivatives have been widely used in conjunction with ESR spectroscopy to detect cellular superoxide with some success. However, the formation of unstable superoxide adducts from the reaction of cyclic nitrones with superoxide is a stumbling block in detecting superoxide by using electron spin resonance (ESR). A chemiluminescent probe, lucigenin, and fluorogenic probes, hydroethidium and MitoSox, are the other frequently used methods in detecting superoxide. However, luceginen undergoes redox-cycling producing superoxide by itself, and hydroethidium and MitoSox react with other oxidants apart from superoxide forming red fluorescent products contributing to artefacts in these assays. Hence, both methods were deemed to be inappropriate for superoxide detection. In this study, an effective approach, a selective mechanism-based colorimetric detection of superoxide anion has been developed by using silylated azulenyl nitrones spin traps. Since a nitrone moiety and an adjacent silyl group react readily with radicals and oxygen anions respectively, such nitrones can trap superoxide efficiently because superoxide is both a radical and an oxygen anion. Moreover, the synthesized nitrone is designed to be triggered solely by superoxide and not by other commonly observed oxygen radicals such as hydroxyl radical, alkoxyl radicals and peroxyl radical. In vitro studies have shown that these synthesized silylated azylenyl nitrones and the mitochondrial-targeted guanylhydrazone analog can trap superoxide efficiently yielding UV-vis identifiable and even potentially fluorescence-detectable orange products. Therefore, the chromotropic detection of superoxide using these nitrones can be a promising method in contrast to other available methods.

Optimal selection of autoregressive model coefficients for early damage detectability with an application to wind turbine blades

Piotr Omenzetter and Simon Hoell's work within the Lloyd's Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research.

Optimal selection of autoregressive model coefficients for early damage detectability with an application to wind turbine blades

Piotr Omenzetter and Simon Hoell's work within the Lloyd's Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd’s Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research.

Effect of road quality in structural health monitoring under operational conditions

The effect of unevenness in a bridge deck for the purpose of Structural Health Monitoring (SHM) under operational conditions is studied in this paper. The moving vehicle is modelled as a single degree of freedom system traversing the damaged beam at a constant speed. The bridge is modelled as an Euler-Bernoulli beam with a breathing crack, simply supported at both ends. The breathing crack is treated as a nonlinear system with bilinear stiffness characteristics related to the opening and closing of crack. The unevenness in the bridge deck considered is modelled using road classification according to ISO 8606:1995(E). Numerical simulations are conducted considering the effects of changing road surface classes from class A - very good to class E - very poor. Cumulant based statistical parameters, based on a new algorithm are computed on stochastic responses of the damaged beam due to passages of the load in order to calibrate the damage. Possibilities of damage detection and calibration under benchmarked and non-benchmarked cases are considered. The findings of this paper are important for establishing the expectations from different types of road roughness on a bridge for damage detection purposes using bridge-vehicle interaction where the bridge does not need to be closed for monitoring.

Effects of vehicle speed in structural health monitoring from operational conditions of bridges

The effects of vehicle speed for Structural Health Monitoring (SHM) of bridges under operational conditions are studied in this paper. The moving vehicle is modelled as a single degree oscillator traversing a damaged beam at a constant speed. The bridge is modelled as simply supported Euler-Bernoulli beam with a breathing crack. The breathing crack is treated as a nonlinear system with bilinear stiffness characteristics related to the opening and closing of crack. The unevenness of the bridge deck is modelled using road classification according to ISO 8606:1995(E). The stochastic description of the unevenness of the road surface is used as an aid to monitor the health of the structure in its operational condition. Numerical simulations are conducted considering the effects of changing vehicle speed with regards to cumulant based statistical damage detection parameters. The detection and calibration of damage at different levels is based on an algorithm dependent on responses of the damaged beam due to passages of the load. Possibilities of damage detection and calibration under benchmarked and non-benchmarked cases are considered. Sensitivity of calibration values is studied. The findings of this paper are important for establishing the expectations from different vehicle speeds on a bridge for damage detection purposes using bridge-vehicle interaction where the bridge does not need to be closed for monitoring. The identification of bunching of these speed ranges provides guidelines for using the methodology developed in the paper.

Extracting bridge frequencies from dynamic responses of two passing vehicles

A conventional way to identify bridge frequencies is utilizing vibration data measured directly from the bridge. A drawback with this approach is that the deployment and maintenance of the vibration sensors are generally costly and time-consuming. One of the solutions is in a drive-by approach utilizing vehicle vibrations while the vehicle passes over the bridge. In this approach, however, the vehicle vibration includes the effect of road surface roughness, which makes it difficult to extract the bridge modal properties. This study aims to examine subtracting signals of two trailers towed by a vehicle to reduce the effect of road surface roughness. A simplified vehicle-bridge interaction model is used in the numerical simulation; the vehicle - trailer and bridge system are modeled as a coupled model. In addition, a laboratory experiment is carried out to verify results of the simulation and examine feasibility of the damage detection by the drive-by method.

AN ENTROPIC THEORY OF DAMAGE WITH APPLICATIONS TO CORROSION-FATIGUE STRUCTURAL INTEGRITY ASSESSMENT

This dissertation demonstrates an explanation of damage and reliability of critical components and structures within the second law of thermodynamics. The approach relies on the fundamentals of irreversible thermodynamics, specifically the concept of entropy generation due to materials degradation as an index of damage. All failure mechanisms that cause degradation, damage accumulation and ultimate failure share a common feature, namely energy dissipation. Energy dissipation, as a fundamental measure for irreversibility in a thermodynamic treatment of non-equilibrium processes, leads to and can be expressed in terms of entropy generation. The dissertation proposes a theory of damage by relating entropy generation to energy dissipation via generalized thermodynamic forces and thermodynamic fluxes that formally describes the resulting damage. Following the proposed theory of entropic damage, an approach to reliability and integrity characterization based on thermodynamic entropy is discussed. It is shown that the variability in the amount of the thermodynamic-based damage and uncertainties about the parameters of a distribution model describing the variability, leads to a more consistent and broader definition of the well know time-to-failure distribution in reliability engineering. As such it has been shown that the reliability function can be derived from the thermodynamic laws rather than estimated from the observed failure histories. Furthermore, using the superior advantages of the use of entropy generation and accumulation as a damage index in comparison to common observable markers of damage such as crack size, a method is proposed to explain the prognostics and health management (PHM) in terms of the entropic damage. The proposed entropic-based damage theory to reliability and integrity is then demonstrated through experimental validation. Using this theorem, the corrosion-fatigue entropy generation function is derived, evaluated and employed for structural integrity, reliability assessment and remaining useful life (RUL) prediction of Aluminum 7075-T651 specimens tested.

FREQUENCY DOMAIN CHARACTERIZATION OF OPTIC FLOW AND VISION-BASED OCELLAR SENSING FOR ROTATIONAL MOTION

The structure of an animal’s eye is determined by the tasks it must perform. While vertebrates rely on their two eyes for all visual functions, insects have evolved a wide range of specialized visual organs to support behaviors such as prey capture, predator evasion, mate pursuit, flight stabilization, and navigation. Compound eyes and ocelli constitute the vision forming and sensing mechanisms of some flying insects. They provide signals useful for flight stabilization and navigation. In contrast to the well-studied compound eye, the ocelli, seen as the second visual system, sense fast luminance changes and allows for fast visual processing. Using a luminance-based sensor that mimics the insect ocelli and a camera-based motion detection system, a frequency-domain characterization of an ocellar sensor and optic flow (due to rotational motion) are analyzed. Inspired by the insect neurons that make use of signals from both vision sensing mechanisms, advantages, disadvantages and complementary properties of ocellar and optic flow estimates are discussed.

Object detection and recognition in complex scenes

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

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.

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

New Fefe-hydrogenase Genes Identified In A Metagenomic Fosmid Library From A Municipal Wastewater Treatment Plant As Revealed By High-throughput Sequencing.

A fosmid metagenomic library was constructed with total community DNA obtained from a municipal wastewater treatment plant (MWWTP), with the aim of identifying new FeFe-hydrogenase genes encoding the enzymes most important for hydrogen metabolism. The dataset generated by pyrosequencing of a fosmid library was mined to identify environmental gene tags (EGTs) assigned to FeFe-hydrogenase. The majority of EGTs representing FeFe-hydrogenase genes were affiliated with the class Clostridia, suggesting that this group is the main hydrogen producer in the MWWTP analyzed. Based on assembled sequences, three FeFe-hydrogenase genes were predicted based on detection of the L2 motif (MPCxxKxxE) in the encoded gene product, confirming true FeFe-hydrogenase sequences. These sequences were used to design specific primers to detect fosmids encoding FeFe-hydrogenase genes predicted from the dataset. Three identified fosmids were completely sequenced. The cloned genomic fragments within these fosmids are closely related to members of the Spirochaetaceae, Bacteroidales and Firmicutes, and their FeFe-hydrogenase sequences are characterized by the structure type M3, which is common to clostridial enzymes. FeFe-hydrogenase sequences found in this study represent hitherto undetected sequences, indicating the high genetic diversity regarding these enzymes in MWWTP. Results suggest that MWWTP have to be considered as reservoirs for new FeFe-hydrogenase genes.