Current design codes for fire resistance of structures are based on isolated member tests subjected to standard fire conditions. Such tests do not reflect the behaviour of a complete building under either normal temperature or fire conditions. Many aspects of behaviour occur due to the interaction between members and cannot be predicted or observed in tests of isolated elements. Performance of real structures subject to real fires is often much better than that predicted from standard tests due to structural continuity and the provision of alternative load paths. This paper reports on the results of a collaborative research project (Tensile membrane action and robustness of structural steel joints under natural fire, European Community FP5 project HPRI-CV 5535) involving the following institutions: Czech Technical University (Czech Republic), University of Coimbra (Portugal), Slovak Technical University (Slovak Republic) and Building Research Establishment (United Kingdom). It consists of an experimental programme to investigate the global structural behaviour of a compartment on the 8-storey steel-concrete composite frame building at the Cardington laboratory during a BRE large-scale fire test, aimed at the examination of the temperature development within the various structural elements, the corresponding (dynamic) distribution of internal forces and the behaviour of the composite slab, beams, columns and connections. r
Fake news are nowadays an issue of pressing concern, given their recent rise as a potential threat to high-quality journalism and well-informed public discourse. The Fake News Challenge (FNC-1) was organized in early 2017 to encourage the development of machine learning-based classification systems for stance detection (i.e., for identifying whether a particular news article agrees, disagrees, discusses, or is unrelated to a particular news headline), thus helping in the detection and analysis of possible instances of fake news. This article presents a novel approach to tackle this stance detection problem, based on the combination of string similarity features with a deep neural network architecture that leverages ideas previously advanced in the context of learning efficient text representations, document classification, and natural language inference. Specifically, we use bi-directional Recurrent Neural Networks (RNNs), together with max-pooling over the temporal/sequential dimension and neural attention, for representing (i) the headline, (ii) the first two sentences of the news article, and (iii) the entire news article. These representations are then combined/compared, complemented with similarity features inspired on other FNC-1 approaches, and passed to a final layer that predicts the stance of the article towards the headline. We also explore the use of external sources of information, specifically large datasets of sentence pairs originally proposed for training and evaluating natural language inference methods, in order to pre-train specific components of the neural network architecture (e.g., the RNNs used for encoding sentences). The obtained results attest to the effectiveness of the proposed ideas and show that our model, particularly when considering pre-training and the combination of neural representations together with similarity features, slightly outperforms the previous state-of-the-art. 39:2 • Borges et al.is increasingly harder to know for sure what to trust, with the absorption of fake news by the masses having increasingly harmful consequences [48]. Automatically dealing with fake news has drawn considerable attention in several research communities [24,26,34,36,40,41,45]. However, the task of evaluating the veracity of news articles is still very demanding and complex, even for trained specialists and much more for automated systems.A useful first step towards identifying fake news articles relates to understanding what other news agencies, in a given moment, are reporting about the same topic. This sub-task is often referred to as stance detection, and automating this process might be useful in developing automated assistants to help in fact checking. In particular, an automatic approach to stance detection would allow, for example, someone to insert an allegation or a news title, and recover related articles that either agree, disagree, or discuss that title. Then, the human checker would use her own judgment to assess the situation.Based on the aforementioned general ideas, a Fake Ne...
a b s t r a c tRetrofit of existing steel buildings often requires strengthening of the connection regions. One common connection, the bolted beam-column connection, is often strengthened in design using stiffened extended endplates, or with continuity plates welded between the column flanges. In a retrofit scenario, adding stiffeners to the endplate is difficult due to the concrete slab and metal deck, and excessive field welding of continuity plates may be uneconomical. Simplifying retrofit efforts, and for economy, connection strength may be improved by simply adding more bolts to the connection. Current code methods, broadly generalized to all connection configurations, are currently based on component experiments having only one bolt on either side of the column web. This study experimentally investigates strengthening of bolted beam-column connections, having no column web stiffeners, using more than one bolt on either side of the column web. Six full-scale bolted beam-column connections are tested, representing exterior beamcolumn connections (beams attached to only one column flange). Connections with both extended and flush endplates are considered. Two column sections (HE300A and HE300B) are tested along with HE300B beams creating both equal-column-beam, and weak-column strong-beam scenarios. Analytical simulations provide insight into local connection demands, and experimental results are compared with current code methods. The experiments indicate that closer inner-bolt spacing relative to the column web increases connection moment capacity but decreases rotation capacity (connection ductility) due to increased bolt prying forces from column flange distortions. The outer bolt of multiple-bolt-per-row configurations contributes very little to the connection resistance when column web stiffeners are not considered. With the exception of specimen T-3B which failed through bolt-thread shear after 0.02 rad, all connections with multiple bolts per row still achieved rotations greater than 0.06 rad. The Eurocode 3 component method and adapted Eurocode 3 procedures conservatively predicted the connection strength of each test specimen, including weak-column strong-beam assemblies, and accurately identified the initial connection limit states.
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