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Register a new userR.C Structures Evaluation And Behavior Analysis Due to Explosions and Accidental Hazards
استخدام النمذجة الحاسوبية لتحليل سلوك منشآت البيتون المسلح المتضررة بفعل الانفجارات وتقييم سلامتها
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Authors
علي محمد الحسن( باحث )
Created by
Ali Hasan
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يستعرض البحث أضرار الانفجارات على المباني والمنشآت الهندسية وآلية توصيف هذه الأضرار ونمذجة المنشآت باستخدام البرامج الحاسوبية لدراسة طرائق الترميم و توقع سلوك المنشأ المتضرر.
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الدليل الارشادي للكشف والمراقبة على المباني والمنشآت الهندسية لضمان سلامتها الانشائية
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يحتوي البحث على دراسة قمنا بها حول تاثير الانفجارات على منشآت البيتون المسح والسلوك الفعلي لهذه المنشآت نتيجة الأضرار الحاصلة.
بالاضافة لعملية تقييم السلوك الحاصل وتوقع المشاكل المستقبلية ودراستها باستخدام النمذجة الرقمية باعتماد برامج النمذجة.
This work theoretically investigates the nonlinear behaviour of
reinforced concrete dee aimed topcantilever beams with concentrated loads
at their free ends The study is aimed to in investigate the behaviour
and respnse of such deep cantilever bea
ms, and to help structural
engineers to design and adopt appropriate reinforcement detailing of
such elements. A complete review of literature on this subject is
made.
This study has provided an accurate description of the traditional nonlinear
static analysis and modal pushover analysis and then these two analyzes
applied for a set of concrete buildings.
Our Paper is a laboratory modeling research to evaluate the efficiency of finite element model in emulation the behavior of R.C. beams with shear deficiencies (ultimate load, mechanism of cracking and failure, load-deflection behavior) strengthened w
ith GFRP strips.
We tested nine R.C. beams 200x30x16 cm in three groups, the first consists of three R.C.beams for comparing, the second consists of three strengthened R.C. beams with two sides vertical GFRP strips, and the third also consists of three strengthened R.C. beams with two sides inclined (45°) GFRP strips.
We modeled these beams by advanced finite element program Ansys10, and we get results agreed with our laboratory study.
In this paper, we develop Sindhi subjective lexicon using a merger of existing English resources: NRC lexicon, list of opinion words, SentiWordNet, Sindhi-English bilingual dictionary, and collection of Sindhi modifiers. The positive or negative sent
iment score is assigned to each Sindhi opinion word. Afterwards, we determine the coverage of the proposed lexicon with subjectivity analysis. Moreover, we crawl multi-domain tweet corpus of news, sports, and finance. The crawled corpus is annotated by experienced annotators using the Doccano text annotation tool. The sentiment annotated corpus is evaluated by employing support vector machine (SVM), recurrent neural network (RNN) variants, and convolutional neural network (CNN).
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