Use of Artificial Neural Networks for Prediction of Properties of Self-Sensing Concrete

Abstract

Nanomaterials such as carbon nanotubes and carbon nanofibers are used as reinforcement for concrete, enhancing its compressive and flexural strength, and durability, and providing additional properties such as electrical conductivity. Hence, CNT/CNF reinforced concrete composite material is multifunctional material which may be used in structural capacity as well as structural health monitoring purposes. Although this material can be superior to traditional concrete, extensive and costly procedures of fabrication are hindering its practical potential. Concrete mix design methods are commonly used during the design of such composites, however, since these methods cannot give direct connection between the recipe and the end-product, every composite must be put through testing and iteratively adjusted until the appearance of wanted results. This paper proposes application of artificial neural networks for predicting properties of CNT/CNF concrete composite materials. Artificial neural networks in mix design have been developed for various types of concrete, commonly to predict only compressive strength as the primary property of concrete. However, self-sensing concrete is used primarily for its piezoresistivity and enhanced strength is only the consequence of the existence of nanofillers. Hence, the paper investigates prediction of compressive and flexural strength as well as electrical resistivity of 468 concrete mixtures by developing 3 different datasets comprehended by 6 ANN models. The models show some interesting results and point toward the necessity of further investigations on this topic and possible improvements.