Volume 20, Issue 2 (2020)                   MCEJ 2020, 20(2): 55-70 | Back to browse issues page

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Hanteh R, Hanteh M, Kheyroddin A, Rezaifar O. Determination of Strength Parameters in Roller Compacted Concrete (RCC) Dams using Laboratory Results and Forecasting based on Artificial Neural Networks. MCEJ 2020; 20 (2) :55-70
URL: http://mcej.modares.ac.ir/article-16-46458-en.html
1- M.Sc civil Engineering in Water Engineering & Hydraulic Structure, Moshanir Power Engineering Consultants, Iran
2- PhD candidate - Structure Engineering, Semnan University, Semnan, Iran
3- Professor - Civil Engineering Department, Semnan University, Semnan, Iran3 , kheyroddin@semnan.ac.ir
4- Associate Professor - Civil Engineering Department, Semnan University, Semnan, Iran
Abstract:   (2278 Views)
The RCC construction method is one of the alternative methods in constructing concrete and earth dams. RCC is a kind of concrete which is pressed with roller, and after being compacted with roller, it is changed to a concrete like normal concrete .Primarily, using RCC in dam construction was developed to obtain the concrete structural properties and a construction method similar to earth dams. In RCC mix design, compressibility, non-segregation, maintaining the consistency of the fresh concrete, limiting the permeability, and achieving the proper bonding among the layers is required for efficiency .A qualitatively and quantitatively wide range of materials are used for constructing RCC mixtures in different dam projects. Therefore،Primarily the RCC aimed to obtain the conventional concrete properties and the construction procedure similar to earth dams. The quality and accessibility to materials to produce RCC should satisfy the structural and durability requirements. Therefore, the proper ratio of roller compacted concrete Mixture is an important step in achieving an economical and durable concrete.  This study aims to investigate the effects of various factors on the dam RCC Mixture Design (a case study of Javeh dam in the West of Iran), which optimum Mixture Design was developed as a laboratory research while observing all technical requirements and finally it was conducted as a test pad application. Javeh Reservoir Dam is located in a distance of 40 km southwest of Sanandaj, Kurdistan and 6 km from the downstream of Gaveh Roud and Qeshlaq River intersection. The dam is made of RCC. The properties of RCC in fresh and hardened conditions are of particular importance. Technical and economic advantages of RCC dams depend on the suitability of the mix design. Therefore, by an appropriate mix design, we can investigate the effect of RCC composite materials on the strength properties of RCC in terms of compressive strength, efficiency and non-separation characteristics of aggregates and their ratios in an optimal mix design. RCC must have sufficient efficiency to achieve the desired density according to the method and facilities. The efficiency of the new mix of RCC is entirely influenced by the amount of paste in the RCC mix. The paste contains the materials finer than #200 sieve including cement, pozzolan, water, filler of the aggregates and air bubbles. From the results, the total cementitious materials used in the optimum laboratory Mixture Design is 125 kg/m3. In addition, the ratio of Paste to mortar was about 5% higher than the minimum recommended in the RCC instruction given in the manual of the US Army Corps of Engineers. The compressive strength of roller concrete is affected by factors such as the amount of aggregates, the quality of fine grains, the quality of cement materials, grade density and content of mixed moisture. Considering that artificial neural networks are among the modeling methods that have shown great power to adapt to engineering problems, the models for predicting the compressive strength of this type of 180-day concrete is discussed based on the actual results from laboratory Mixture Design by neural networks modeling. The values of correlation coefficients in each of the models made in this study were close to the value of 1, which indicates the appropriate accuracy of the models.
 
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Subject: Earthquake
Received: 2020/09/30 | Accepted: 2020/05/30 | Published: 2020/05/30

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