Experimental analysis of the cyclic response of traditional timber joints and their influence on the seismic capacity of timber frame structures

Timber connections represent the crucial part of a timber structure and a great variability exists in terms of types of connections and mechanisms. Taking as case study the widespread traditional timber frame structures, in particular the Portuguese Pombalino buildings, one of the most common timber connection is the half-lap joint. Connections play a major role in the overall behaviour of a structure, particularly when assessing their seismic response, since damage is concentrated at the connections. For this reason, an experimental campaign was designed and distinct types of tests were carried out on traditional half-lap joints to assess their in-plane response. In particular, pull-out and in-plane cyclic tests were carried out on real scale unreinforced connections. Subsequently, the connections were retrofitted, using strengthening techniques such as self-tapping screws, steel plates and GFRP sheets. The tests chosen were meant to capture the hysteretic behaviour and dissipative capacity of the connections and characterise their response and, therefore, their influence on the seismic response of timber frame walls, particularly concerning their uplifting and rotation capacity, that could lead to rocking in the walls. In this paper, the results of the experimental campaign are presented in terms of hysteretic curves, dissipated energy and equivalent viscous damping ratio. Moreover, recommendations are provided on the most appropriate retrofitting solutions.

Local seismic cultures: the use of timber frame structures in the south of Portugal

Given the fact that using timber frame structures has proven to improve the seismic behavior of vernacular architecture, as has been reported in past earthquakes in many countries, its preservation as a traditional earthquake resistant practice is important. This paper firstly intends to evaluate whether the use of timber frames as a traditional seismic resistant technique for vernacular architecture in the South of Portugal, traditionally a seismic region, is still active. Secondly, the city of Vila Real de Santo António was selected as a case study because it also followed a Pombaline development contemporary to the reconstruction of Lisbon. The plan included the provision of timber frame partition walls for some of the buildings and, thus, an overview of the type of constructions originally conceived is provided. Finally, the alterations done in the original constructions and the current state of the city center are described and the effect of these changes on the seismic vulnerability of the city is discussed.

MixAR mobile prototype: visualizing virtually reconstructed ancient structures in situ

Archeology and related areas have a special interest on cultural heritage sites since they provide valuable information about past civilizations. However, the ancient buildings present in these sites are commonly found in an advanced state of degradation which difficult the professional/expert analysis. Virtual reconstructions of such buildings aim to provide a digital insight of how these historical places could have been in ancient times. Moreover, the visualization of such models has been explored by some Augmented Reality (AR) systems capable of providing support to experts. Their compelling and appealing environments have also been applied to promote the social and cultural participation of general public. The existing AR solutions regarding this thematic rarely explore the potential of realism, due to the following lacks: the exploration of mixed environments is usually only supported for indoors or outdoors, not both in the same system; the adaptation of the illumination conditions to the reconstructed structures is rarely addressed causing a decrease of credibility. MixAR [1] is a system concerned with those challenges, aiming to provide the visualization of virtual buildings augmented upon real ruins, allowing soft transitions among its interiors and exteriors and using relighting techniques for a faithful interior illumination, while the user freely moves in a given cultural heritage site, carrying a mobile unit. Regarding the focus of this paper, we intend to report the current state of MixAR mobile unit prototype, which allows visualizing virtual buildings – properly aligned with real-world structures – based on user's location, during outdoor navigation. In order to evaluate the prototype performance, a set of tests were made using virtual models with different complexities.

Companies' OHS internal structures: Types and theoretical foundations

An organization manages to regulate, standardize and optimize its operations in a way that places health and safety first by considering Health, Safety, Environment and Ergonomics (HSEE). In order to provide a theoretical foundation for Companies’ Occupational Health and Safety Internal Structures (COHSIS) allowing the focus on safety, COHSIS and its fundamentals were reviewed and described based on a brief literature review, which have included both papers published in journals, as well as works made available by selected organizations with mature safety culture. Three COHSIS were defined for description of its fundamentals, enabling improve the usability of its terminologies and provide the mentioned theoretical foundation.

Creep behaviour of cracked steel fibre reinforced self-compacting concrete laminar structures

Tese de Doutoramento - Civil Engineering

High strain rate constitutive modeling forhistorical structures subjected to blast loading

Doctoral Thesis Civil Engineering

A cost study of common surface finish systems for steel structures

This paper presents part of a study aimed at finding a suitable, yet cost-effective, surface finish for a steel structure subject to the car washing environment and corrosive chemicals. The initial, life cycle and average equivalent annual (AEAC) costs for surface finishing methods were calculated for a steel structure using the LCCC algorithm developed by American Galvanizers Association (AGA). The cost study consisted of 45 common surface finish systems including: hot-dip galvanization (HDG), metallization, acrylic, alkyd and epoxy as well as duplex coatings such as epoxy zinc and inorganic zinc (IOZ). The results show that initial, life cycle and AEAC costs for hot dip galvanization are the lowest among all the other methods, followed by coal tar epoxy painting. The annual average cost of HDG for this structure was estimated about €0.22/m2, while the other cost-effective alternatives were: IOZ, polyurea, epoxy waterborne and IOZ/epoxy duplex coating.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Designing cnc knit for hybrid membrane and bending active structures

Recent advances in computation allow for the integration of design and simulation of highly interrelated systems, such as hybrids of structural membranes and bending active elements. The engaged complexities of forces and logistics can be mediated through the development of materials with project specific properties and detailing. CNC knitting with high tenacity yarn enables this practice and offers an alternative to current woven membranes. The design and fabrication of an 8m high fabric tower through an interdisciplinary team of architects, structural and textile engineers, allowed to investigate means to design, specify, make and test CNC knit as material for hybrid structures in architectural scale. This paper shares the developed process, identifies challenges, potentials and future work.

Enhanced resistive switching and multilevel behavior in bilayered HfAlO/HfAlOx structures for non-volatile memory applications

In this work, hafnium aluminum oxide (HfAlO) thin films were deposited by ion beam sputtering deposition technique on Si substrate. The presence of oxygen vacancies in the HfAlOx layer deposited in oxygen deficient environment is evidenced from the photoluminescence spectra. Furthermore, HfAlO(oxygen rich)/HfAlOx(oxygen poor) bilayer structures exhibit multilevel resistive switching (RS), and the switching ratio becomes more prominent with increasing the HfAlO layer thickness. The bilayer structure with HfAlO/HfAlOx thickness of 30/40 nm displays the enhanced multilevel resistive switching characteristics, where the high resistance state/ intermediate resistance state (IRS) and IRS/low resistance state resistance ratios are 102 and 5 105 , respectively. The switching mechanisms in the bilayer structures were investigated by the temperature dependence of the three resistance states. This study revealed that the multilevel RS is attributed to the coupling of ionic conduction and the metallic conduction, being the first associated to the formation and rupture of conductive filaments related to oxygen vacancies and the second with the formation of a metallic filament. Moreover, the bilayer structures exhibit good endurance and stability in time.

Redes de inovação em pequenas e médias empresas: um estudo de caso no setor têxtil

Dissertação de mestrado em Engenharia Industrial

An innovative framework for probabilistic-based structural assessment with an application to existing reinforced concrete structures

A novel framework for probabilistic-based structural assessment of existing structures, which combines model identification and reliability assessment procedures, considering in an objective way different sources of uncertainty, is presented in this paper. A short description of structural assessment applications, provided in literature, is initially given. Then, the developed model identification procedure, supported in a robust optimization algorithm, is presented. Special attention is given to both experimental and numerical errors, to be considered in this algorithm convergence criterion. An updated numerical model is obtained from this process. The reliability assessment procedure, which considers a probabilistic model for the structure in analysis, is then introduced, incorporating the results of the model identification procedure. The developed model is then updated, as new data is acquired, through a Bayesian inference algorithm, explicitly addressing statistical uncertainty. Finally, the developed framework is validated with a set of reinforced concrete beams, which were loaded up to failure in laboratory.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.