Abstract: The floor beams of steel buildings, cold-formed steel
floor joists in particular, often require large web openings, which may
affect their shear capacities. A cost effective way to mitigate the
detrimental effects of such openings is to weld/fasten reinforcements.
A difficulty associated with an experimental investigation to establish
suitable reinforcement schemes for openings in shear zone is that
moment always coexists with the shear, and thus, it is impossible to
create pure shear state in experiments, resulting in moment
influenced results. However, Finite Element Method (FEM) based
analysis can be conveniently used to investigate the pure shear
behaviour of webs including webs with reinforced openings. This
paper presents the details associated with the finite element analysis
of thick/thin-plates (representing the web of hot-rolled steel beam,
and the web of a cold-formed steel member) having a large
reinforced opening. The study considered simply-supported
rectangular plates subjected to in-plane shear loadings until failure
(including post-buckling behaviour). The plate was modelled using
geometrically non-linear quadrilateral shell elements, and non-linear
stress-strain relationship based on experiments. Total Langrangian
with large displacement/small strain formulation was used for such
analyses. The model also considered the initial geometric
imperfections. This study considered three reinforcement schemes,
namely, flat, lip, and angle reinforcements. This paper discusses the
modelling considerations and presents the results associated with the
various reinforcement schemes under consideration.
Abstract: Consumer-to-Consumer (C2C) E-commerce has been
growing at a very high speed in recent years. Since identical or
nearly-same kinds of products compete one another by relying on
keyword search in C2C E-commerce, some sellers describe their
products with spam keywords that are popular but are not related to
their products. Though such products get more chances to be retrieved
and selected by consumers than those without spam keywords,
the spam keywords mislead the consumers and waste their time.
This problem has been reported in many commercial services like
ebay and taobao, but there have been little research to solve this
problem. As a solution to this problem, this paper proposes a method
to classify whether keywords of a product are spam or not. The
proposed method assumes that a keyword for a given product is
more reliable if the keyword is observed commonly in specifications
of products which are the same or the same kind as the given
product. This is because that a hierarchical category of a product
in general determined precisely by a seller of the product and so is
the specification of the product. Since higher layers of the hierarchical
category represent more general kinds of products, a reliable degree
is differently determined according to the layers. Hence, reliable
degrees from different layers of a hierarchical category become
features for keywords and they are used together with features only
from specifications for classification of the keywords. Support Vector
Machines are adopted as a basic classifier using the features, since
it is powerful, and widely used in many classification tasks. In
the experiments, the proposed method is evaluated with a golden
standard dataset from Yi-han-wang, a Chinese C2C E-commerce,
and is compared with a baseline method that does not consider
the hierarchical category. The experimental results show that the
proposed method outperforms the baseline in F1-measure, which
proves that spam keywords are effectively identified by a hierarchical
category in C2C E-commerce.