An Extreme Learning Machine with feature selection for estimating mechanical properties of lightweight aggregate concretes

In this paper, a Particle Swarm Optimization algorithm is used to adjust the parameters of an Extreme Learning Machine and select features in order to predict mechanical properties of lightweight aggregate concretes. Unlike the approaches found in the literature, the proposed procedure set the model parameters and select the most beneficial subset of features while simultaneously estimates two important outcomes: the compressive strength and elasticity modulus. These properties can be modeled as a function of up to four features: water/cement fraction, lightweight aggregate volume, cement quantity and lightweight aggregate density. The Particle Swarm Optimization algorithm performs the parameter and feature selection and automatically tunes the number of neurons in the hidden layer and the activation function. The results are compared with a model selection based on exhaustive search on the parameter space. The proposed approach arises as an alternative tool to select the most relevant features and to estimate the mechanical properties of lightweight aggregate concretes.