A performance-based method for seismic evaluation of masonry walls strengthened with RC layers

A conventional method for seismic strengthening of masonry walls is externally application of reinforced concrete layer (shotcrete). However, due to the lack of analytical and experimental information on the behavior of strengthened walls, the design procedures are usually followed based on the empirical relations. Using these design procedures have resulted in massive strengthening details in retrofitting projects. This paper presents a computational framework for nonlinear analysis of strengthened masonry walls and its versatility has been verified by comparing the numerical and experimental results. Based on the developed numerical model and available experimental information, design relations and failure modes are proposed for strengthened walls in accordance with the ASCE 41 standard. Finally, a sample masonry structure has been strengthened using the proposed and available conventional methods. It has been shown that using the proposed method results in lower strengthening details and appropriate (ductile) failure modes

Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer Science

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

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.

Segmentação do tecido tumoral cerebral em imagens de ressonância magnética

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)

Avaliação quantitativa da geodiversidade: desenvolvimento de instrumentos metodológicos com aplicação ao ordenamento do território

Tese de Doutoramento em Ciências (Especialidade de Geologia)

Simultaneous detection of cyclopiazonic acid and aflatoxin B1 by HPLC in methanol/water mobile phase

A simple procedure for the simultaneous detection of cyclopiazonic acid (CPA) and aflatoxin B1 from fungal extracts is presented, using a methanol and water mobile phase and fluorescence detection. This methodology has been tested with standard solutions of both mycotoxins CPA and Aflatoxin B1 and with methanolic extracts of Aspergillus section Flavi strains, previously characterized for their mycotoxin production profile. Previously available methodology required the use of two different chromatographic runs for these mycotoxins, with distinct columns and detectors (fluorescence detection with a post-column photochemical derivatization (PHRED) for aflatoxin B1 and UV detection for CPA). The proposed method detects both mycotoxins in a single run. Data from these assays will be presented and discussed.