Artificial Intelligence (AI) mechanisms are more and more frequently applied to all sorts of civil engineering problems. New methods and algorithms which allow civil engineers to use these techniques in a different way on diverse problems are available or being made available. One AI techniques stands out over the rest: Artificial Neural Networks (ANN). Their most remarkable traits are their ability to learn, the possibility of generalization and their tolerance towards mistakes. These characteristics make their use viable and cost-efficient in any field in general, and in Structural Engineering in particular. The most extended construction material nowadays is concrete, mainly because of its high resistance and its adaptability to formwork during its fabrication process. Along this chapter we will find different applications of ANNs to structural concrete.
Artificial Neural Networks
Warren McCulloch and Walter Pitts are credited for the origin of Artificial Networks in the 1940s, since they were the first to design an artificial neuron (McCulloch & Pitts, 1943). They proposed the binary mode (active or inactive) neuron model with a fixed threshold which must be surpassed for it to change state. Some of the concepts they introduced still hold useful today.
Artificial Neural Networks intend to simulate the properties found in biological neural systems through mathematical models by the way of artificial mechanisms. A neuron is considered a formal element, or module, or basic network unit which receives information from other modules or the environment; it then integrates and computes this information to emit a single output which will be identically transmitted to subsequent multiple neurons (Wasserman, 1989).
The output of an artificial neuron is determined by its propagation or excitation, activation and transfer functions.
The propagation function is generally the summation of each input multiplied by the weight of its interconnection (net value):
(1)The activation function modifies the latter, relating the neural input to the next activation state.
ai(
t) =
FA[
ai(
t – 1),
ni(
t – 1)]
(2)The transfer function is applied to the result of the activation function. It is used to bound the neuron’s output and is generally given by the interpretation intended for the output. Some of the most commonly used transfer functions are the sigmoid (to obtain values in the [0,1] interval) and the hyperbolic tangent (to obtain values in the [-1,1] interval).
outi = FT(
ai(
t))
(3)Once each element in the process is defined, the type of network (network topology) to use must be designed. These can be divided in forward-feed networks, where information moves in one direction only (from input to output), and networks with partial or total feedback, where information can flow in any direction.
Finally, learning rules and training type must be defined. Learning rules are divided in supervised and non-supervised (Brown & Harris, 1994) (Lin & Lee, 1996) and within the latter, self-organizing learning and reinforcement learning (Hoskins & Himmelblau, 1992). The type of training will be determined by the type of learning chosen.