Prediction of Mechanical Properties of Frozen Soils Using Response Surface Method: An Optimization Approach

Abstract

The present study was based on a promoting statistical method known as response surface method (RSM). RSM has been applied as an efficient method to optimize many physical applications in industry for more than two decades. In the current study, the RSM was utilized as a platform to develop models as a function of some prescribed input factors to predict mechanical properties (responses) of frozen soils (i.e. peak tensile/compressive strength, elasticity modulus). Besides, RSM makes it possible to find significant factors and probable interactions as well. A widespread literature review was conducted and three case studies were chosen to evaluate the performance of the RSM in developing precise models and finally an optimum experiment. For each case study, less than half of the available data (an average of 40.8%) was employed to develop models and the remaining part was employed to evaluate the validity of derived models. A comparison between predicted and measured data showed a good agreement with a significant level of 0.05. This indicates that upon using the model a hundred times to predict an specific property for different input factors, the maximum five predictions may diverge from the measured values with ± confidence interval. In addition, some contours were plotted to give a comprehensive presentation of any probable correlations between investigated properties and input factors. Based on the developed models with an average correlation coefficients (R 2) of 93.69, temperature was found to be the most significant factor affecting the mechanical properties of frozen fine soil, while the dry density was not as effective as the temperature.

Design of Experiment (DOE) Analysis with Response Surface Method (RSM) to Optimize the Electroplating Parameter

“…The model in Equation ( 1) is known as the Scheffé polynomials model. It can find the interaction between experimented factors (Vahdani et al, 2020). Moreover, the significance value of variables and their interactions will be determined by the Analysis of Variance (ANOVA).…”

“…DOE successfully determines the most important factor or variable to control to achieve optimal process performance (Kazemain, Ebrahimi-Nejad, & Jaafarian, 2018). DOE is also a powerful tool for quantitative analysis in experimental design which is employed through Central Composite Design (CCD), Response Surface Method (RSM), full factorial analysis, Taguchi design, and so on (Vahdani, Ghazavi, & Roustaei, 2020).…”

Process Parameter Optimization of 3D-Printer Machine Using Response Surface Method for Printing Hydroxyapatite/Collagen Composite Slurry

“…Design of experiment (DoE) as a collection of data is adopted by RSM [29]. RSM was introduced by Box and Wilson in 1951 [30,31]. It is useful not only in the engineering sector but also in a variety of other fields [32].…”

Impact of Logging Speed on Sedimentary Rock Identification Based on Long and Short Density Log

“…Comparison of density logs and laboratory tests of rock density in oil fields shows a strong correlation [3]. Temperature has been shown to be a significant factor in the changes of mechanical properties of fine soil [4]. Optimum porosity for cavity and groove obstacle in sedimentary rock is 22% and 19%, respectively [5].…”