Structural FRF acquisition via electric impedance measurement applied to damage location

Continuing development of new materials makes systems lighter and stronger permitting more complex systems to provide more functionality and flexibility that demands a more effective evaluation of their structural health. Smart material technology has become an area of increasing interest in this field. The combination of smart materials and artificial neural networks can be used as an excellent tool for pattern recognition, turning their application adequate for monitoring and fault classification of equipment and structures. In order to identify the fault, the neural network must be trained using a set of solutions to its corresponding forward Variational problem. After the training process, the net can successfully solve the inverse variational problem in the context of monitoring and fault detection because of their pattern recognition and interpolation capabilities. The use of structural frequency response function is a fundamental portion of structural dynamic analysis, and it can be extracted from measured electric impedance through the electromechanical interaction of a piezoceramic and a structure. In this paper we use the FRF obtained by a mathematical model (FEM) in order to generate the training data for the neural networks, and the identification of damage can be done by measuring electric impedance, since suitable data normalization correlates FRF and electrical impedance.

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Damage Detection in Flexible Plate Using Lamb Waves

This paper presents an experimental technique for structural health monitoring (SHM) based on Lamb waves approach in an aluminum plate using piezoelectric material as actuators and sensors. Lamb waves are a form of elastic perturbation that remains guided between two parallel free surfaces, such as the upper and lower surfaces of a plate, beam or shelf. Lamb waves are formed when the actuator excites the surface of the structure with a pulse after receiving a signal. Two PZTs were placed in the plate surface and one of them was used to send a predefined wave through the structure. Thus, the other PZT (adjacent) becomes the sensor. Using this methodology, this paper presents one case of damage detection considering the aluminum plate in the free-free-free-free boundary condition. The damage was simulated by adding additional mass on the plate. It is proposed two damage detection indexes obtained from the experimental signal, involving the Fast Fourier Transform (FFT) and the power spectral density (PSD) that were computed using the output signal. The results show the viability of the presented methodology to damage detection in smart structures

Structural damage identification and location using grammian matrices

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

DNA damage in patients who underwent minimally invasive surgery under inhalation or intravenous anesthesia

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation of DNA damage and lipoperoxidation of propofol in patients undergoing elective surgery

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Lower levels of oxidative DNA damage and apoptosis in lymphocytes from patients undergoing surgery with propofol anesthesia

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Relationships between cagA, vacA, and iceA genotypes of Helicobacter pylori and DNA damage in the gastric mucosa

Helicobacter pylori (H. pylori) is believed to dispose carriers to gastric cancer by inducing chronic inflammation. The inflammatory processes may result in the generation of reactive oxygen and nitrogen species that damage DNA. In this study, we investigated the relationships between DNA damage in the gastric mucosa and cogA, vocA, and iceA genotypes of H. pylori. The study was conducted with biopsies from the gastric antrum and corpus of 98 H. pylori-infected and 26 uninfected control patients. H. pylori genotypes were determined by PCR and DNA damage was measured in gastric mucosal cells by the Comet assay (single cell gel electrophoresis). All patients were nonsmokers, not abusing alcohol, and not using prescription or recreational drugs. Levels of DNA damage were significantly higher (P < 0.0001) in the H. pylori-infected patients than in uninfected patients. In comparison with the level of DNA damage in the uninfected controls, the extent of DNA damage in both the antrum (OR = 8.45; 95% Cl 2.33-37.72) and the corpus (OR 6.55; 95% Cl 2.52-17.72) was related to infection by cagA(+)/vocAs1m1 and iceA1 strains. The results indicate that the genotype of H. pylori is related to the amount of DNA damage in the gastric mucosa. These genotypes could serve as biomarkers for the risk of extensive DNA damage and possibly gastric cancer. (C) 2004 Wiley-Liss, Inc.

Relationship among oxidative DNA damage, gastric mucosal density and the relevance of cagA, vacA and iceA genotypes of Helicobacter pylori

The aim of this study was to evaluate the relationship among oxidative DNA damage, density of Helicobacter pylori and the relevance of cagA, vacA and iceA genotypes of H. pylori. Gastric epithelial cells were isolated from 24 uninfected patients, 42 H. pylori infected patients with gastritis, and 61 patients with gastric cancer. Oxidative DNA damage was analyzed by the Comet assay, the density of H. pylori was measured by real-time polymerase chain reaction (PCR), and allelic variants of cagA, vacA and iceA were identified using the PCR. Infected patients by Helicobacter pylori cagA(+), vacAs1 m1 and iceA1 genotype showed higher levels of oxidative DNA damage than infected patients with H. pylori cagA(-), vacAs2 m2 and iceA2 genotypes and uninfected patients. Density of H. pylori did not influence oxidative DNA damage. Our results indicate that H. pylori genotype is more relevant than density for oxidative DNA damage.

Avaliação de impacto ambiental e gestão dos recursos natuarias no estuário Apodi Mossoró, nordeste do Brasil

The aim of this study is to investigate the eco-environmental vulnerability, its changes, and its causes to develop a management system for application of eco-environmental vulnerability and risk assessment in the Apodi-Mossory estuary, Northeast Brazil. This analysis is focused on the interference of the landscape conditions, and its changes, due to the following factors: the oil and natural gas industry, tropical fruits industry, shrimp farms, marine salt industry, occupation of the sensitive areas; demand for land, vegetation degradation, siltation in rivers, severe flooding, sea level rise (SLR), coastal dynamics, low and flat topography, high ecological value and tourism in the region and the rapid growth of urbanization. Conventional and remote sensing data were analyzed using modeling techniques based on ArcGIS, ER-Mapper, ERDAS Imagine and ENVI software. Digital images were initially processed by Principal Component Analysis and transformation of the maximum fraction of noise, and then all bands were normalized to reduce errors caused by bands of different sizes. They were integrated in a Geographic Information System analysis to detect changes, to generate digital elevation models, geomorphic indices and other variables of the study area. A three band color combination of multispectral bands was used to monitor changes of land and vegetation cover from 1986 to 2009. This task also included the analysis of various secondary data, such as field data, socioeconomic data, environmental data and prospects growth. The main objective of this study was to improve our understanding of eco-environmental vulnerability and risk assessment; it´s causes basically show the intensity, its distribution and human-environment effect on the ecosystem, and identify the high and low sensitive areas and area of inundation due to future SLR, and the loss of land due to coastal erosion in the Apodi-Mossoró estuary in order to establish a strategy for sustainable land use. The developed model includes some basic factors such as geology, geomorphology, soils, land use / land cover, vegetation cover, slope, topography and hydrology. The numerical results indicate that 9.86% of total study area was under very high vulnerability, 29.12% high vulnerability, 52.90% moderate vulnerability and 2.23% were in the category of very low vulnerability. The analysis indicates that 216.1 km² and 362.8 km² area flooded on 1m and 10m in sea levels respectively. The sectors most affected were residential, industrial and recreational areas, agricultural land, and ecosystems of high environmental sensitivity. The results showed that changes in eco-environmental vulnerability have a significant impact on the sustainable development of the RN state, since the indicator is a function of sensitivity, exposure and status in relation to a level of damage. The model were presented as a tool to assist in indexing vulnerability in order to optimize actions and assess the implications of decisions makers and policies regarding the management of coastal and estuarine areas. In this context aspects such as population growth, degradation of vegetation, land use / land cover, amount and type of industrialization, SLR and government policies for environmental protection were considered the main factors that affect the eco-environmental changes over the last three decades in the Apodi-Mossoró estuary.

AT1-receptor mediated vascular damage in myocardium, kidneys and liver in rats

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)