• Computers and Concrete
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• v.15 no.2
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• pp.215-229
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• 2015

The emerging technology of self compacting concrete, fiber reinforcement together reduces vibration and substitute conventional reinforcement which help in improving the economic efficiency of the construction. The objective of this work is to find the regression model to determine the response surface of mix proportioning Steel Fiber Reinforced Self Compacting Concrete (SFSCC) using statistical investigation. A total of 30 mixtures were designed and analyzed based on Design of Experiment (DOE). The fresh properties of SCC and mechanical properties of concrete were studied using Response Surface Methodology (RSM). The results were analyzed by limited proportion of fly ash, fiber, volume combination ratio of two steel fibers with aspect ratio of 50/35: 60/30 and super plasticizer (SP) dosage. The center composite designs (CCD) have selected to produce the response in quadratic equation. The model responses included in the primary stage were flowing ability, filling ability, passing ability and segregation index whereas in harden stage of concrete, compressive strength, split tensile strength and flexural strength at 28 days were tested. In this paper, the regression model and the response surface plots have been discussed, and optimal results were found for all the responses.

• Advances in aircraft and spacecraft science
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• v.6 no.6
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• pp.529-550
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• 2019

The effect of multiple process parameters on a set of continuous response variables is, especially in experimental designs, difficult and intricate to determine. Due to the complexity in aeroacoustic and vibroacoustic studies, the often-performed simple one-factor-at-a-time method turns out to be the least effective approach. In contrast, the statistical Design of Experiments is a technique used with the objective to maximize the obtained information while keeping the experimental effort at a minimum. The presented work aims at giving insights on Design of Experiments applied to aeroacoustic and vibroacoustic problems while comparing different experimental designs and approximation models. For this purpose, an experimental rig of a ducted low-pressure fan is developed that allows gathering data of both, aerodynamic and aeroacoustic nature while analysing three independent process parameters. The experimental designs used to sample the design space are a Central Composite design and a Box-Behnken design, both used to model a response surface regression, and Latin Hypercube sampling to model an Artificial Neural network. The results indicate that Latin Hypercube sampling extracts information that is more diverse and, in combination with an Artificial Neural network, outperforms the quadratic response surface regressions. It is shown that the Latin Hypercube sampling, initially developed for computer-aided experiments, can also be used as an experimental design. To further increase the benefit of the presented approach, spectral information of every experimental test point is extracted and Artificial Neural networks are chosen for modelling the spectral information since they show to be the most universal approximators.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.207-208
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• 2010

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.49-54
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• 2006

When using response surface designs, the experimental trials should be carried out in blocks in case of heterogeneity of conditions. When we use nearly orthogonal blocking, we need evaluate the degree of orthogonal blocking. Graphical methods for evaluating the degree of orthogonal blocking are suggested.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.243-248
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• 1995

The problem of optimal design for a nonparametric regression with binary data is considered. The aim of the statistical analysis is the estimation of a quantal response surface in two dimensions. Bias, variance and IMSE of kernel estimates are derived. The optimal design density with respect to asymptotic IMSE is constructed.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.669-675
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• 2006

When using response surface designs, the experimental trials should be carried out in blocks in case of heterogeneity of conditions. When we use nearly orthogonal blocking, we need evaluate the degree of orthogonal blocking. Graphical methods for evaluating the degree of orthogonal blocking are suggested.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.267-280
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• 2007

Various single-valued design optimality criteria such as D-, G-, and V-optimality are used often in constructing optimal experimental designs for mixture experiments in a constrained region R where lower and upper bound constraints are imposed on the ingredients proportions. Even though they are optimal in the strict sense of particular optimality criterion used, it is known that their performance is unsatisfactory with respect to the prediction capability over a constrained region. (Vining et at., 1993; Khuri et at., 1999) We assume the quadratic polynomial model as the mixture response surface model and are interested in finding efficient designs in the constrained design space for a mixture. In this paper, we make an expanded list of candidate design points by adding interior points to the extreme vertices, edge midpoints, constrained face centroids and the overall centroid. Then, we want to propose a robust design with respect to D-optimality, G-optimality, V-optimality and distance-based U-optimality. Comparing scaled prediction variance quantile plots (SPVQP) of robust designs with that of recommended designs in Khuri et al. (1999) and Vining et al. (1993) in the well-known examples of a four-component fertilizer experiment as well as McLean and Anderson's Railroad Flare Experiment, robust designs turned out to be superior to those recommended designs.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.595-605
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• 2002

The central composite design is widely used for estimating second order response surfaces. This type of design is composed of $2^k$ factorial points, axial points and center points. In this paper, we suggest a version of central composite design where the positions of the axial points are indicated by two numbers, and study properties of this design. We obtain the variances and covariances of the estimators of the regression coefficients. Conditions are obtained for this design to be orthogonal and rotatable. This design is compared with other designs on the basis of efficiency.

• International Journal of Reliability and Applications
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• v.2 no.3
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• pp.209-240
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• 2001

Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.

• Journal of the Korean Statistical Society
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• v.11 no.1
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• pp.36-44
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• 1982

A new design concept, called axis-slope-rotatability, is presented for the design of experiments with mixtures. This is an analogue of the Box-Hunter (1957) rotatability for second order response surface designs. By choice of design, it is possible to make the variance of the estimated slopes along the component axes constant for all axial points equidistant from the center point of the factor space. This property is called axis-slope-rotatability for mixture experiments. When the Scheffe's second degree polynomial is used, it is shown that some symmetry conditions are sufficient for axis-slope-rotatability. Several designs having this property are illustrated.