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 : Farzan Mahboubi (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Evaluating the settlement of lightweight coarse aggregate in self-compacting lightweight concrete
Moosa Mazloom,Farzan Mahboubi 사단법인 한국계산역학회 2017 Computers and Concrete, An International Journal Vol.19 No.2
The purpose of this paper is to evaluate the settlement of lightweight coarse aggregate of self-compacting lightweight concrete (SCLC) after placement of concrete on its final position. To investigate this issue, sixteen samples of concrete mixes were made. The water to cementitious materials ratios of the mixes were 0.35 and 0.4. In addition to the workability tests of self-compacting concrete (SCC) such as slump flow, V-funnel and L-box tests, a laboratory experiment was made to examine the segregation of lightweight coarse aggregate in concrete. Because of the difficulties of this test, the image processing technique of MATLAB software was used to check the segregation above too. Moreover, the fuzzy logic technique of MATLAB software was utilized to improve the clarity of the borders between the coarse aggregate and the paste of the mixtures. At the end, the results of segregation tests and software analyses are given and the accuracy of the software analyses is evaluated. It is worth noting that the minimum and maximum differences between the results of laboratory tests and software analyses were 1.2% and 9.19% respectively. It means, the results of image processing technique looks exact enough for estimating the segregation of lightweight coarse aggregate in SCLC.
-
Moosa Mazloom,Saeed Farahani Tajar,Farzan Mahboubi 사단법인 한국계산역학회 2020 Computers and Concrete, An International Journal Vol.25 No.5
Artificial neural networks are used as a useful tool in distinct fields of civil engineering these days. In order to control long-term quality of Self-Compacting Semi-Lightweight Concrete (SCSLC), the 90 days compressive strength is considered as a key issue in this paper. In fact, combined artificial neural networks are used to predict the compressive strength of SCSLC at 28 and 90 days. These networks are able to re-establish non-linear and complex relationships straightforwardly. In this study, two types of neural networks, including Radial Basis and Multilayer Perceptron, were used. Four groups of concrete mix designs also were made with two water to cement ratios (W/C) of 0.35 and 0.4, as well as 10% of cement weight was replaced with silica fume in half of the mixes, and different amounts of superplasticizer were used. With the help of rheology test and compressive strength results at 7 and 14 days as inputs, the neural networks were used to estimate the 28 and 90 days compressive strengths of above-mentioned mixes. It was necessary to add the 14 days compressive strength in the input layer to gain acceptable results for 90 days compressive strength. Then proper neural networks were prepared for each mix, following which four existing networks were combined, and the combinatorial neural network model properly predicted the compressive strength of different mix designs.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
