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Founded in 1904 and headquartered in Farmington Hills, Michigan, USA, the American Concrete Institute is a leading authority and resource worldwide for the development, dissemination, and adoption of its consensus-based standards, technical resources, educational programs, and proven expertise for individuals and organizations involved in concrete design, construction, and materials, who share a commitment to pursuing the best use of concrete.
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Home > Publications > International Concrete Abstracts Portal
The International Concrete Abstracts Portal is an ACI led collaboration with leading technical organizations from within the international concrete industry and offers the most comprehensive collection of published concrete abstracts.
Title: Novel Approach for Concrete Mixture Design Using Neural Dynamics Model and Virtual Lab Concept
Author(s): Mohammad Hossein Rafiei, Waleed H. Khushefati, Ramazan Demirboga, and Hojjat Adeli
Publication: Materials Journal
Appears on pages(s): 117-127
Keywords: cost optimization; enhanced probabilistic neural network; genetic algorithm; mixture design; neural dynamics model; neural networks
Abstract:To solve the concrete mixture design problem, engineers have traditionally relied on guidelines such as those from ACI, and a
conservative, labor-intensive, time-consuming, and costly trialand-error approach that neglects cost or environmental impact of the mixture in the design procedure. In this paper, the concrete mixture design problem is solved through adroit integration of a nonlinear optimization algorithm (OA) and a computational intelligence-based classification algorithm (CA) used as a virtual lab to predict whether desired constraints are satisfied in each iteration or not. The model is tested using previously collected data, three OAs, and three CAs. The outcome of this research is an entirely new paradigm and methodology for concrete mixture design for the twenty-first century. The most cost-effective solutions are achieved by the combination of neural dynamics model of Adeli and Park and enhanced probabilistic neural networks. The cost savings for large-scale concrete projects can be in the millions of dollars.
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