A hybrid artificial neural network-based identification systemfor fine-grained composites

Abstract

Recent interest in the development of innovative building materials has brought about the need for a detailed assessment of their mechanical fracture properties. The parameters for these need to be acquired, and one of the possible ways of doing so is to obtain them indirectly - based on a combination of fracture testing and inverse analysis. The paper describes a method for the identification of selected parameters of mortars and other fine-grained brittle matrix composites. The cornerstone of the method is the use of an artificial neural network, which is utilized as a surrogate model of the inverse relation between the measured specimen response parameters and the sought material parameters. Due to the potentially wide range of composite mixtures and hence the wide range of experimental responses likely to be gained from individual specimens, an ensemble of artificial neural networks was created. It allows the entire range of variants to be covered and provides resulting parameter values with sufficient precision. Such a system is also easy to expand if a composite with properties outside the current range is tested. The capabilities of the proposed identification system are demonstrated on two selected types of fine-grained composites with different specimen responses. The first group of specimens was made of composite based on alkali-activated slag with standardized and natural sand investigated within the time interval of 3 to 330 days of aging. The second tested composite contained alkali-activated fly ash matrix, and the effect of the addition of natural fibers on fracture response was investigated.