• Computers and Concrete
• /
• v.16 no.1
• /
• pp.99-123
• /
• 2015

In this study, structural vulnerability of reinforced concrete moment resisting frames (RC-MRFs) by considering the Iran-specific characteristics is investigated to manage the earthquake risk in terms of multicomponent seismic excitations. Low and medium rise RC-MRFs, which constitute approximately 80-90% of the total buildings stock in Iran, are focused in this fragility-based assessment. The seismic design of 3-12 story RC-MRFs are carried out according to the Iranian Code of Practice for Seismic Resistant Design of Buildings (Standard No. 2800), and the analytical models are formed accordingly in open source nonlinear platforms. Frame structures are categorized in three subclasses according to the specific characteristics of construction practice and the observed seismic performance after major earthquakes in Iran. Both far and near fields' ground motions have been considered in the fragility estimation. An optimal intensity measure (IM) called Sa, avg and beta probability distribution were used to obtain reliable fragility-based database for earthquake damage and loss estimation of RC buildings stock in urban areas of Iran. Nonlinear incremental dynamic analyses by means of lumped-parameter based structural models have been simulated and performed to extract the fragility curves. Approximate confidence bounds are developed to represent the epistemic uncertainties inherent in the fragility estimations. Consequently, it's shown that including vertical ground motion in the analysis is highly recommended for reliable seismic assessment of RC buildings.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.11a
• /
• pp.465-467
• /
• 2022

Mobile phones have become an essential item nowadays since it provides access to online platform and service fast and easy. Coming to these platforms such as Social Network Service (SNS) for shopping have been a go-to option for many people. However, searching for a specific fashion item in the picture is challenging, where users need to try multiple searches by combining appropriate search keywords. To tackle this problem, we propose a system that could provide immediate access to websites related to fashion items. In the framework, we also propose a deep learning model for an automatic analysis of image contexts using instance segmentation. We use transfer learning by utilizing Deep fashion 2 to maximize our model accuracy. After segmenting all the fashion item objects in the image, the related search information is retrieved when the object is clicked. Furthermore, we successfully deploy our system so that it could be assessable using any web browser. We prove that deep learning could be a promising tool not only for scientific purpose but also applicable to commercial shopping.