scholarly journals Study on Betel Nut Fiber Enhancing Water Stability of Asphalt Mixture Based on Response Surface Method

2022 ◽  
pp. e00870
Author(s):  
Zhijie Tang ◽  
Youwei Gan ◽  
Ting Yu ◽  
Chuangmin Li
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Asphalt Mixture ◽  
Water Stability ◽  
Betel Nut ◽  
Surface Method

scholarly journals A New Eco-Friendly Porous Asphalt Mixture Modified by Crumb Rubber and Basalt Fiber

2019 ◽  
Vol 11 (20) ◽  
pp. 5754 ◽  
Author(s):  
Yongchun Cheng ◽  
Chao Chai ◽  
Yuwei Zhang ◽  
Yu Chen ◽  
Bing Zhu
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Asphalt Mixture ◽  
Basalt Fiber ◽  
Crumb Rubber ◽  
High Temperature Stability ◽  
Porous Asphalt ◽  
Particle Loss ◽  
Independent Variables ◽  
Surface Method

In this paper, the performance of environmentally friendly porous asphalt mixture was optimized by the response surface method. Taking the asphalt-aggregate ratio, crumb-rubber content, and basalt fiber content as the independent variables, the air void, Marshall stability, flow value, Marshall quotient, and Cantabro particle loss are the response values. The best model was determined by fitting the experimental data. After the influence of the independent variables on the response values was clarified, the models were used to optimize the dosage of the asphalt, crumb rubber, and basalt fiber through comprehensive analysis. The results showed that the application of the response surface method can complete the establishment of the models and the optimization of the performance of the porous asphalt mixture with sufficient accuracy. The optimum dosage of the asphalt to aggregate ratio, crumb rubber, and basalt fiber is 4.51%, 11.21%, and 0.42%, respectively. The high-temperature stability, low-temperature crack resistance, water stability, and Cantabro particle loss resistance of the optimized porous asphalt mixture were effectively improved, which provides a reference for the construction of eco-friendly pavement.

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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