Concrete is a durable and versatile construction
material. It is not only Strong, economical and takes the shape of the form in
which it is placed, but it is also aesthetically satisfying. However
experience has shown that concrete is vulnerable to deterioration, unless
precautionary measures are taken during the design and production. For this we
need to understand the influence of components on the behaviour of concrete and
to produce a concrete mix within closely controlled tolerances. High performance concrete is a concrete mixture, which
possess high durability and high strength when compared to conventional
Concrete mix companies have extensive records
of their past mix proportions, which can be used to develop a model for the
design procedure. Automation of the mix proportioning can be carried out with
different soft computing techniques. Soft computing is a conglomerate of
computing techniques that include fuzzy-based methods, neuro-computing, genetic
computing, probabilistic reasoning, genetic algorithms, chaotic systems, belief
networks, and learning theory . The soft computing techniques effectively
explore the relationship among independent and dependent variables without any
assumptions about the relationship between the various variables.
An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural
network that is
based on Takagi–Sugeno fuzzy inference system.
The technique was developed in the early 1990s. Since it integrates both
neural networks and fuzzy logic principles,
it has potential to capture the benefits of both in a single framework.
Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to
approximate nonlinear functions. Hence, ANFIS is considered to be a
universal estimator. For using the ANFIS in a more efficient and optimal
way, one can use the best parameters obtained by genetic algorithm.
Current study introduced ANFIS as a tool to
develop a fuzzy model that can estimate compressive strength of high
performance concrete given its mix proportion. The results are verified using
root mean square error value of compressive strength value of model.