http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Vellasco, Marley (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
Structural assessment of cold-formed composite structures
P.C.G. da S. Vellasco,A.J.R. Mergulhão,S.A.L. de Andrade 국제구조공학회 2002 Steel and Composite Structures, An International J Vol.2 No.5
The main aim of the present paper is to present the results of a full-scale experimental investigation to study the structural behaviour of composite steel beams. The composite beam was made of cold-formed steel section shapes filled with reinforced concrete. First a comprehensive description of the experimental results in terms of: deflections, deformations, slippage and stress levels on critical steps of the load path is presented. The experimental results were then compared to theoretical values obtained by the use of an analytical model based on ultimate limit state stress blocks. Finally, a practical application of the use of this structural solution is depicted.
-
Porosity estimation by semi-supervised learning with sparsely available labeled samples
Lima, Luiz Alberto,Gö,rnitz, Nico,Varella, Luiz Eduardo,Vellasco, Marley,Mü,ller, Klaus-Robert,Nakajima, Shinichi Elsevier 2017 Computers & geosciences Vol.106 No.-
<P><B>Abstract</B></P> <P>This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generally suffer from extremely expensive (and hence sparsely available) porosity samples. To optimally make use of the valuable porosity data, a semi-supervised machine learning method was proposed, <I>Transductive Conditional Random Field Regression</I> (TCRFR), showing good performance (Görnitz et al., 2017). TCRFR, however, still requires more labeled data than those usually available, which creates a gap when applying the method to the porosity estimation problem in realistic situations. In this paper, we aim to fill this gap by introducing two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios. Our new method outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data, proving its potential as a practical industrial tool.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A porosity estimation method with simultaneous facies classification is proposed. </LI> <LI> The method combines the benefits of ridge regression and conditional random fields. </LI> <LI> Two preprocessing techniques are introduced, inspired from image processing. </LI> </UL> </P>
-
Behaviour of flush end-plate beam-to-column joints under bending and axial force
Luís Simões da Silva,Luciano R. O. de Lima,Pedro C. G. da S. Vellasco,Sebastião A. L. de Andrade 국제구조공학회 2004 Steel and Composite Structures, An International J Vol.4 No.2
Steel beam-to-column joints are often subjected to a combination of bending and axial forces. The level of axial forces in the joint may be significant, typical of pitched-roof portal frames, sway frames or frames with incomplete floors. Current specifications for steel joints do not take into account the presence of axial forces (tension and/or compression) in the joints. A single empirical limitation of 10% of the beam’s plastic axial capacity is the only enforced provision in Annex J of Eurocode 3. The objective of the present paper is to describe some experimental and numerical work carried out at the University of Coimbra to try to extend the philosophy of the component method to deal with the combined action bending moment and axial force.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
