Efficiency Of Response Surface Method Research Articles

Long-term deformation analysis of Shuibuya concrete face rockfill dam based on response surface method and improved genetic algorithm

Due to the size effects of rockfill materials, the settlement difference between numerical simulation and in situ monitoring of rockfill dams is a topic of general concern. The constitutive model parameters obtained from laboratory triaxial tests often underestimate the deformation of high rockfill dams. Therefore, constitutive model parameters obtained by back analysis were used to calculate and predict the long-term deformation of rockfill dams. Instead of using artificial neural networks (ANNs), the response surface method (RSM) was employed to replace the finite element simulation used in the optimization iteration. Only 27 training samples were required for RSM, improving computational efficiency compared with ANN, which required 300 training samples. RSM can be used to describe the relationship between the constitutive model parameters and dam settlements. The inversion results of the Shuibuya concrete face rockfill dam (CFRD) show that the calculated settlements agree with the measured data, indicating the accuracy and efficiency of RSM.

Reliability Analysis on Structures Based on a Modified Iterative Response Surface Method

In order to improve the computational accuracy and efficiency of response surface method for reliability analysis on structures, a modified iterative response surface method (called as NDIRSM) is proposed. Firstly, a new starting center point, which is closer to design point, is calculated out as the starting center point instead of the point at mean values of input variables and a dynamic factor vector f1, which is inversely proportional to the change rate of performance function with respect to variance, is calculated out for the first iteration. Then the arbitrary factors fk are determined according to the design matrix condition number for the subsequent iteration. Thus the sample points are close to limit state function and the response surface function can approximate the limit state function accurately and efficiently. Two examples are employed to validate the advantages of NDIRSM and the results show that NDIRSM improves the computational accuracy and efficiency of response surface method. At last, NDIRSM is applied to the reliability analysis on low cycle fatigue life of a gas turbine disc, which provides a useful reference for reliability analysis on low cycle fatigue life of gas turbine disc and demonstrates the high computational accuracy and efficiency of NDIRSM.

Application of Box–Behnken Design in response surface methodology for adsorptive removal of arsenic from aqueous solution using CeO2/Fe2O3/graphene nanocomposite

A novel CeO2/Fe2O3/graphene nanocomposite was synthesized by the hydrothermal process and used for arsenic removal. Box-Behnken Design was employed to optimize adsorptive removal of As(III) and As(V) from aqueous solutions. The instrumental technique like SEM, EDX, TEM, HRTEM, XRD, BET and FTIR spectrometer were used to characterize the material. Batch adsorption experiments were conducted with three input variables including initial concentration (10–50 mg/L), adsorbent dosage (0.100–0.200 g/L), and pH (3−10). Regression analysis indicated the good fitness of experimental data to a quadratic polynomial model with the determination coefficient (R2) value of 0.9957 and 0.9929 for As(III) and As(V). Model accuracy was confirmed by ANOVA and lack-of-fit test. Optimum conditions for maximum removal of As(III) (Initial concentration = 10.52 mg/L, adsorbent dose = 0.198 g/L and pH = 7.84) and As(V) (Initial concentration = 10.78 mg/L, adsorbent dose = 0.197 g/L and pH = 3.05) were analyzed by the model. The removal percentage of As(III) and As(V) was found to be 98.53% and 97.26% in the optimum condition. The results confirmed the high efficiency of response surface method for the removal of arsenic from aqueous solutions by CeO2/Fe2O3/graphene nanocomposite.