Study of Concrete having Industrial Waste as Fine Aggregate Replacement and Generation of Model for Prediction of Compressive Strength Using Response Surface Method

2017 ◽  
Vol 4 (9) ◽  
pp. 9727-9731 ◽  
Author(s):  
Sohan Gupta ◽  
R.K. Tripathi ◽  
R.K. Mishra
Keyword(s):  
Compressive Strength ◽  
Response Surface ◽  
Response Surface Method ◽  
Industrial Waste ◽  
Fine Aggregate ◽  
Surface Method

scholarly journals Experimental Study on Proportioning Optimized Ultra-Fine Aggregate Cementing Materials with Full Gold Tailings Using Response Surface Method

2021 ◽  
Author(s):  
Jingyi Liu ◽  
Zhenjun Zhou ◽  
Zhihua Sun ◽  
Jiuran Wen ◽  
Kaiping Liu
Keyword(s):  
Compressive Strength ◽  
Response Surface ◽  
Response Surface Method ◽  
Concrete Strength ◽  
Reducing Agent ◽  
Relative Mass ◽  
Fine Aggregate ◽  
Visual Model ◽  
Surface Method ◽  
Negative Linear Correlation

In order to prepare the ultra-fine full gold tailing aggregate cementing materials, the gold tailing powders were mixed with cement under different proportions. Then the effect of sand quantity, water quantity and dosage of water-reducing agent on the compressive strength of the full tailing aggregate cementing materials was studied using response surface method in Design-Expert software. Based on the regression analysis of test results, a visual model was provided. The research results show that when the relative mass dosage of cement is 1, the amount of sand is greater than 1.1, the amount of water is greater than 0.24, and the amount of water reducing agent is 0.01. The 7d compressive strength of tailings concrete shows a significant negative linear correlation with the amount of sand and water. That implays the concrete strength decreases with the increase of the amount of sand and water. When the relative mass of cement is 1, the maximum 7d strength of concrete appears at the relative mass of sand 1.0, the amount of water is 0.22, and the amount of water reducer is 0.01. The maximum 7d compressive strength is about 75.43MPa.

Optimization of Compressive Strength of Reactive Powder Concrete for an Energy Storage Pile Application Using Response Surface Method

2019 ◽  
Vol 950 ◽  
pp. 117-122
Author(s):  
Umut Bektimirova ◽  
Eldar Sharafutdinov ◽  
Aidana Tleuken ◽  
Chang Seon Shon ◽  
Di Chuan Zhang ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Energy Storage ◽  
Response Surface ◽  
Response Surface Method ◽  
Plain Concrete ◽  
Linear Interpolation ◽  
Reactive Powder Concrete ◽  
Surface Method ◽  
Binder Ratio ◽  
Reactive Powder

The main goal of this study was to optimize the compressive strength of reactive powder concrete (RPC) for an energy storage pile application using response surface method (RSM). The compressive strength of 9 different RPC mixtures along with 3 plain concrete mixtures was determined. Silica fume (SF) content and the water-to-binder ratio (w/b) were selected as parameters to influence the compressive strength of the concrete mixture. RSM regression analysis was used to develop a prediction model of compressive strength. Based on test results and linear interpolation, the combination of 20.46% SF and w/b=0.20 was determined to achieve the highest compressive strength.

A Proposal of PSO with Presumption Function of the Solution Landscape by Response Surface Method

2014 ◽  
Vol 134 (9) ◽  
pp. 1293-1298
Author(s):  
Toshiya Kaihara ◽  
Nobutada Fuji ◽  
Tomomi Nonaka ◽  
Yuma Tomoi
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Surface Method

scholarly journals Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3552 ◽  
Author(s):  
Chun-Yi Zhang ◽  
Jing-Shan Wei ◽  
Ze Wang ◽  
Zhe-Shan Yuan ◽  
Cheng-Wei Fei ◽  
...  
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Tip Clearance ◽  
Strong Nonlinearity ◽  
Surface Method ◽  
Blade Tip ◽  
Blade Tip Clearance

To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.

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