Quality Gain-Loss Function and Its Empirical Analysis in Dam Concrete Construction

Wang, B.; Zhou, H.G.; Li, Z.Y.; Fan, T.Y., and Nie, X.T., 2020. Quality gain-loss function and its empirical analysis in dam concrete construction. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Re...

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Bibliographic Details
Published in:Journal of coastal research 2020-09, Vol.104 (sp1), p.842-848
Main Authors: Wang, Bo, Zhou, Hougui, Li, Zhiyong, Fan, Tianyu, Nie, Xiangtian
Format: Article
Language:English
Subjects:
Analysis
Coastal inlets
Coastal management
Coastal research
Coastal waters
Coastal zone management
Concrete construction
Concrete dams
Construction
Dam construction
Design
Design parameters
Design techniques
deviation degree
Empirical analysis
Gain-Loss
Marine sciences
Marine technology
PHYSICAL AND SOCIAL GEOSCIENCES
process mean optimization
quality compensation
Quality control
Quality gain-loss function
skewed distribution
Skewed distributions
Statistical analysis
Statistical methods
Water resources
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Summary:Wang, B.; Zhou, H.G.; Li, Z.Y.; Fan, T.Y., and Nie, X.T., 2020. Quality gain-loss function and its empirical analysis in dam concrete construction. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 842–848. Coconut Creek (Florida), ISSN 0749-0208. For the quality offset effect that cannot be described by Taguchi quality loss function, the concept of quality gain-loss function (QGLF) is presented in this paper. In considering the interaction between multiple quality characteristics, multivariate quality gain-loss function (M-QGLF) is constructed. The process mean design method of quality gain-loss is discussed under asymmetric conditions respectively. Through an example, the optimum process mean design method is analyzed under the condition of quality characteristic following skewed distribution, and the optimum process mean modified formula and deviation degree calculation formula are proposed. The presentation of QGLF creates the preconditions for the quantitative statistical analysis of quality fluctuation and quality compensation, and provides a new theoretical basis for parameter design. It will also bring great benefit to the production quality improvement as well as get significant economic effect.
ISSN:0749-0208
1551-5036
DOI:10.2112/JCR-SI104-144.1