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

Volume: 114

Issue: 1

Appears on pages(s): 117-127

Keywords: cost optimization; enhanced probabilistic neural network; genetic algorithm; mixture design; neural dynamics model; neural networks

DOI: 10.14359/51689485

Date: 1/1/2017

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.