Design and Analysis of Experiments

Dean, Angela; Voss, Dan; Draguljić, Danel

doi:10.1007/978-3-319-52250-0

Cited by 337 publications

(350 citation statements)

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“…9 with Gauss Reduction Method [2], the regression constants were determined as follows; β o = -1.065; β 1 = -1.9; β 2 = 19.91 and β 3 = 0.604 (10) The objective function of the optimization operation is given in Eq. 11, R + 1.065x1 + 1.9x 2 -19.91x 3 -0.604x 4 …”

Section: From the Laboratory Results Inmentioning

confidence: 99%

“…The technical approach was carried out in three phases; preparation of materials (lateritic soil and waste paper ash), laboratory exercises [15] and formulation of the parametric equations and models of the results and the application of the nonlinear multiple regression relationship and the simplex linear programming (SLP) [1,2,4,[24][25][26][27][28].…”

Section: Technical Approachmentioning

confidence: 99%

“…The mathematical operation of obtaining the optimal maximum and optimal minimum of the effect of a process or procedure in an engineering mechanism is called optimization [1][2][3][4][5][6]. In the field of geotechnical engineering and the area of soil stabilization and improvement in particular, the primary target is the achievement of the lateritic soil strength properties that will ensure the durability and safety of civil engineering projects founded on soils [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Nanostructured Waste Paper Ash Treated Lateritic Soil and Its California Bearing Ratio Optimization

Onyelowe¹

2017

Global J Technol Optim

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The stabilization potentials of NWPA and the CBR optimization were investigated on the treated olokoro lateritic soil. The soil was classified as an A-2-7 soil according to AASHTO classification method. From the stabilization procedure, it has been found that the admixture improved the strength characteristics of the stabilized lateritic soil for use as base material in pavement construction. With the laboratory results, a nonlinear regression relationship was formulated through the multiple regression algorithms for the California bearing ratio (R) as a dependent variable with optimum water content (w), maximum dry density (D), and percentage by weight additive of NWPA (SA) as the independent variables. The nonlinear relationship was linearized to enable the optimization operation with Simplex Linear Programming (Optimization) to be conducted. This iteration procedure was conducted and the results showed that the CBR (R) was optimized at R max =219.16% with x 1 = 48.103, x 2 =4.833, x 3 =13.45, and x 4 = 0.948 in the stabilization of lateritic soils with NWPA as an admixture applied in the percentages of 0, 3, 6, 9, 12, and 15%.

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Section: From the Laboratory Results Inmentioning

confidence: 99%

Section: Technical Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nanostructured Waste Paper Ash Treated Lateritic Soil and Its California Bearing Ratio Optimization

Onyelowe¹

2017

Global J Technol Optim

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“…Angela and Voss [43] and Oehlert [44] provide in-depth descriptions of the methodology. In this work, the popular central composite design (CCD)-namely, the face-centered approach-is used.…”

Section: Data Points For the Experimentsmentioning

confidence: 99%

Surface Modelling of Nanostructured Copper Subjected to Erosion-Corrosion

Irfan

Mukras

Al-Mufadi

et al. 2017

Metals

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Abstract:The last decade has witnessed considerable advancements in nanostructured material synthesis and property characterization. However, there still exists some deficiency in the mechanical and surface property characterization of these materials. In this paper, the erosion corrosion (E-C) behavior of nanostructured copper was studied. The nanostructured copper was produced through severe plastic deformation (SPD) by applying four passes of equal channel angular pressing (ECAP). The combined effects of the testing time, impact velocity, and concentration of erosive solid particles (i.e., sand concentration) on the E-C behavior of nanostructured copper were then examined. Based on a defined domain for the testing time, impact velocity, and sand concentration, E-C tests were performed for numerous combinations of test points via the slurry pot method. The test points were selected using the face-centered center composite design of experiments to enable visualization of the test results through surface plots. The extent of E-C on the test specimens was determined by measuring the mass loss. Polynomial regression and Kriging were used to fit surfaces to the experimental data, which were subsequently used to generate surface plots. The results showed that the E-C of nanostructured copper is best described by a quadratic function of testing time, velocity, and erosive solid particle concentration. The results also revealed that E-C increases with an increasing testing time, impact velocity, and erosive solid particle concentration. In addition, it was observed that the effect of the erosive solid particles on E-C is further intensified by an increased impact velocity.

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“…A second role played by the Friedman test is to implement in a natural way a blocking design [5]. Blocking is an effective way for normalizing the costs observed on different conditions.…”

Section: The F-race Algorithmmentioning

confidence: 99%

Combining Lazy Learning, Racing and Subsampling for Effective Feature Selection

Bontempi

Birattari

Meyer

Adaptive and Natural Computing Algorithms

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This paper presents a wrapper method for feature selection that combines Lazy Learning, racing and subsampling techniques. Lazy Learning (LL) is a local learning technique that, once a query is received, extracts a prediction by locally interpolating the neighboring examples of the query which are considered relevant according to a distance measure. Local learning techniques are often criticized for their limitations in dealing with problems with high number of features and large samples. Similarly wrapper methods are considered prohibitive for large number of features, due to the high cost of the evaluation step. The paper aims to show that a wrapper feature selection method based on LL can take advantage of two effective strategies: racing and subsampling. While the idea of racing was already proposed by Maron and Moore, this paper goes a step further by (i) proposing a multiple testing technique for less conservative racing (ii) combining racing with sub-sampling techniques.

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Design and Analysis of Experiments

Cited by 337 publications

References 0 publications

Nanostructured Waste Paper Ash Treated Lateritic Soil and Its California Bearing Ratio Optimization

Nanostructured Waste Paper Ash Treated Lateritic Soil and Its California Bearing Ratio Optimization

Surface Modelling of Nanostructured Copper Subjected to Erosion-Corrosion

Combining Lazy Learning, Racing and Subsampling for Effective Feature Selection

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