A spectrum-driven damage Identification technique : application and validation through the numerical simulation of the Z24 Bridge

The present paper focuses on a damage identification method based on the use of the second order spectral properties of the nodal response processes. The explicit dependence on the frequency content of the outputs power spectral densities makes them suitable for damage detection and localization. The well-known case study of the Z24 Bridge in Switzerland is chosen to apply and further investigate this technique with the aim of validating its reliability. Numerical simulations of the dynamic response of the structure subjected to different types of excitation are carried out to assess the variability of the spectrum-driven method with respect to both type and position of the excitation sources. The simulated data obtained from random vibrations, impulse, ramp and shaking forces, allowed to build the power spectrum matrix from which the main eigenparameters of reference and damage scenarios are extracted. Afterwards, complex eigenvectors and real eigenvalues are properly weighed and combined and a damage index based on the difference between spectral modes is computed to pinpoint the damage. Finally, a group of vibration-based damage identification methods are selected from the literature to compare the results obtained and to evaluate the performance of the spectral index.

Sensores de fibra ótica baseados em novos materiais flexíveis

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e de Computadores

Numerical Assessment of the out-of-plane response of a brick masonry structure without box behaviour

This paper presents the assessment of the out-of-plane response due to seismic loading of a masonry structure without rigid diaphragm. This structure corresponds to real scale brick masonry specimen with a main façade connected to two return walls. Two modelling approaches were defined for this evaluation. The first one consisted on macro modelling, whereas the second one on simplified micro modelling. As a first step of this study, static nonlinear analyses were conducted to the macro model aiming at evaluating the out-of-plane response and failure mechanism of the masonry structure. A sensibility analyses was performed in order to assess the mesh size and material model dependency. In addition, the macro models were subjected to dynamic nonlinear analyses with time integration in order to assess the collapse mechanism. Finally, these analyses were also applied to a simplified micro model of the masonry structure. Furthermore, these results were compared to experimental response from shaking table tests. It was observed that these numerical techniques simulate correctly the in-plane behaviour of masonry structures. However, the

A firefly dynamic penalty approach for solving engineering design problems

Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.

Dynamic interface model for masonry walls subjected to high strain rate out-of-plane loads

The present study proposes a dynamic constitutive material interface model that includes non-associated flow rule and high strain rate effects, implemented in the finite element code ABAQUS as a user subroutine. First, the model capability is validated with numerical simulations of unreinforced block work masonry walls subjected to low velocity impact. The results obtained are compared with field test data and good agreement is found. Subsequently, a comprehensive parametric analysis is accomplished with different joint tensile strengths and cohesion, and wall thickness to evaluate the effect of the parameter variations on the impact response of masonry walls.

Optimization of magnetoelectric composites based on electroactive polymers for energy harvesting and sensor application

Tese de Doutoramento em Engenharia de Materiais.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Optimization of peptide nucleic acid fluorescence in situ hybridization (PNA-FISH) for the detection of bacteria: the effect of pH, dextran sulfate and probe concentration

Fluorescence in situ hybridization (FISH) is a molecular technique widely used for the detection and characterization of microbial populations. FISH is affected by a wide variety of abiotic and biotic variables and the way they interact with each other. This is translated into a wide variability of FISH procedures found in the literature. The aim of this work is to systematically study the effects of pH, dextran sulfate and probe concentration in the FISH protocol, using a general peptide nucleic acid (PNA) probe for the Eubacteria domain. For this, response surface methodology was used to optimize these 3 PNA-FISH parameters for Gram-negative (Escherichia coli and Pseudomonas fluorescens) and Gram-positive species (Listeria innocua, Staphylococcus epidermidis and Bacillus cereus). The obtained results show that a probe concentration higher than 300 nM is favorable for both groups. Interestingly, a clear distinction between the two groups regarding the optimal pH and dextran sulfate concentration was found: a high pH (approx. 10), combined with lower dextran sulfate concentration (approx. 2% [w/v]) for Gram-negative species and near-neutral pH (approx. 8), together with higher dextran sulfate concentrations (approx. 10% [w/v]) for Gram-positive species. This behavior seems to result from an interplay between pH and dextran sulfate and their ability to influence probe concentration and diffusion towards the rRNA target. This study shows that, for an optimum hybridization protocol, dextran sulfate and pH should be adjusted according to the target bacteria.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.