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RISS 인기검색어
- 검색키워드
- 저자 : A.A. Ramezanianpour (검색결과 4 건)
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Prediction of lightweight concrete strength by categorized regression, MLR and ANN
S. Tavakkol,F. Alapour,A. Kazemian,A. Hasaninejad,A. Ghanbari,A.A. Ramezanianpour 사단법인 한국계산역학회 2013 Computers and Concrete, An International Journal Vol.12 No.2
Prediction of concrete properties is an important issue for structural engineers and different methods are developed for this purpose. Most of these methods are based on experimental data and use measured data for parameter estimation. Three typical methods of output estimation are Categorized Linear Regression (CLR), Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In this paper a statistical cleansing method based on CLR is introduced. Afterwards, MLR and ANN approaches are also employed to predict the compressive strength of structural lightweight aggregate concrete. The valid input domain is briefly discussed. Finally the results of three prediction methods are compared to determine the most efficient method. The results indicate that despite higher accuracy of ANN, there are some limitations for the method. These limitations include high sensitivity of method to its valid input domain and selection criteria for determining the most efficient network.
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A.A. Ramezanianpour,M. Jafari Nadooshan,M. Peydayesh,A. M. Ramezanianpour 대한토목학회 2014 KSCE JOURNAL OF CIVIL ENGINEERING Vol.18 No.1
Salt scaling is a major damage problem for concrete pavements in cold environments. Salt scaling is a type of superficial damage caused by freezing and thawing a saline solution on the surface of concrete. Effects of a new composite Portland cement and air void on deicer scaling resistance of concrete were investigated in this paper. Another objective is to investigate effects of compressive strength, tensile strength, abrasion resistance and water penetration on the freeze-thaw deicer salt scaling. Specimens were tested for freeze-thaw deicer salt scaling resistance in accordance with ASTM C672 test method. Surface strengths of concrete play an important role in salt scaling resistance. There is no appropriate relationship between compressive strength and salt scaling resistance,when concrete mixtures are made with various cementitious materials. Results reveal that the mixture containing composite Portland cement with entrained air bubbles has the best performance in salt scaling test.
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A.A. Ramezanianpour,A. Kazemian,E. Radaei,H. AzariJafari,M.A. Moghaddam 사단법인 한국계산역학회 2014 Computers and Concrete, An International Journal Vol.13 No.4
Ground Granulated Blast Furnace Slag (GGBFS) is widely used as an effective partial cement replacement material. GGBFS inclusion has already been proven to improve several performance characteristics of concrete. GGBFS provides enhanced durability, including high resistance to chloride penetration and protection against alkali silica reaction. In this paper results of an experimental research work on influence of low-reactivity GGBFS (which is largely available in Iran) on the properties of mortars and concretes are reported. In the first stage, influence of GGBFS replacement level and fineness on the compressive strength of mortars was investigated using Taguchi method. The analysis of mean (ANOM) statistical approach was also adopted to develop the optimal conditions. Next, based on the obtained results, concrete mixtures were designed and water penetration, capillary absorption, surface resistivity, and compressive strength tests were carried out on high-strength concrete specimens at different ages up to 90 days. The results indicated that 7-day compressive strength is adversely affected by GGBFS inclusion, while the negative effect is less evident at later ages. Also, it was inferred that use of low-reactivity GGBFS (at moderate levels such as 20% and 30%) can enhance the impermeability of high-strength concrete since 28 days age.
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A.A. Ramezanianpour,M.E. Kamel,E. Ghiasvand,H. Shokrani,N. Bakhshi,A. Kazemian 사단법인 한국계산역학회 2012 Computers and Concrete, An International Journal Vol.10 No.6
This paper presents the results of an investigation on the compressive strength and weight loss of mortars containing three types of fillers as cement replacements; Limestone Filler (LF), Silica Fume (SF) and Trass (TR), subjected to elevated temperatures including 400oC, 600oC, 800oC and 1000oC. Results indicate that addition of TR to blended cements, compared to SF addition, leads to higher compressive strength and lower weight loss at elevated temperatures. In order to model the influence of the different parameters on the compressive strength and the weight loss of specimens, artificial neural networks (ANNs) were adopted. Different diagrams were plotted based on the predictions of the most accurate networks to study the effects of temperature, different fillers and cement content on the target properties. In addition to the impressive RMSE and R2 values of the best networks, the data used as the input for the prediction plots were chosen within the range of the data introduced to the networks in the training phase. Therefore, the prediction plots could be considered reliable to perform the parametric study.
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