Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks

Artificial neural networks (ANNs) have been the preferred choice for modeling the complex and nonlinear material behavior where conventional mathematical approaches do not yield the desired accuracy and predictability. Despite their popularity as a universal function approximator and wide range of applications, no specific rules for deciding the architecture of neural networks catering to a specific modeling task have been formulated. The research paper presents a methodology for automated design of neural network architecture, replacing the conventional trial and error technique of finding the optimal neural network. The genetic algorithms (GA) stochastic search has been harnessed for evolving the optimum number of hidden layer neurons, transfer function, learning rate, and momentum coefficient for backpropagation ANN. The methodology has been applied for modeling slump of ready mix concrete based on its design mix constituents, namely, cement, fly ash, sand, coarse aggregates, admixture, and water-binder ratio. Six different statistical performance measures have been used for evaluating the performance of the trained neural networks. The study showed that, in comparison to conventional trial and error technique of deciding the neural network architecture and training parameters, the neural network architecture evolved through GA was of reduced complexity and provided better prediction performance. 1. Introduction Cement concrete is one of the most widely used construction materials in the world today. The material modeling of concrete is a difficult task owing to its composite nature. Although various empirical relationships in the form of regression equations have been derived from experimental results and are widely in use, these do not provide accuracy and predictability wherein the interactions among the number of variables are either unknown, complex, or nonlinear in nature. Artificial neural networks (ANNs), touted as the next generation of computing, have been the preferred choice since the last few decades for modeling unstructured problems pertaining to material behavior. The notable applications of ANN in modeling properties of concrete are implementations in predicting and modeling compressive strength of high performance concrete [1], self-compacting concrete [2], recycled aggregate concrete [3], rubberized concrete [4], fibre reinforced polymer (FRP) confined concrete [5], durability of high performance concrete [6], predicting drying shrinkage of concrete [7], concrete mix design [8], and prediction of elastic modulus of normal and high