There are many methods, such as, 2 level factorial, center composition design, taguchi, and so on, the question is which model is more responsible for DOE and concrete analyzing?
Factorial, composition are for independent variables only. Taguchi is for the case where you want to cope with the noise in the variables - e.g. in the electronics.
As long you want to optmize the compounds (proportions) of the concrete - cement, sand, water in order to improve its let say 28th day strength - you need DOE for constrained mixtures. May be it can be combined with some independent variables e.g. the quantity of the reinforcement. Mixture - because you have to vary the components of the mixture (cemente, sand etc), constrained - because 0 % of cement for instance have no sense - see:
http://books.google.bg/books/about/Experiments_with_Mixtures.html?id=piWpe3yEAjYC&redir_esc=y
Ali,
Generally saying, It depends on many parameters like the number of factors and goals, etc.
According to my knowledge as a civil engineer who recently started learning DOE, if you are going to investigate many influencing factors it is better to screen the factors and find the significant ones using Plackett-Burman screening design, or 2-level fractional factorial design, etc. I suggest you the fractional factorial design which can detect the interaction between main factors that is considerable issue in concrete. You can also get information about the order (linear or polynomial) of relation between factors and each goal by selecting some center points during fractional factorial design. If there is not one order relation, you can apply RSM to find more precise transfer function between factors and goal.
good luck!
If you are dealing with proportions of materials, you should use Mixture Designs. If one of your components is a "filler", you can use some type of factorial design or response surface.
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