Using ANN to predict post-heating mechanical properties of cementitious composites reinforced with multi-scale additives

Abstract

This paper focuses on predicting the post-heating mechanical properties of cementitious composites reinforced with multi-scale additives using the Artificial Neural Network (ANN) approach. A total of four different feed-forward ANN models are developed using 261 data sets collected from 18 published sources. The models are optimized using 12 input parameters selected based on a comprehensive literature review to predict the residual compressive strength, the residual flexural strengths, elastic modulus, and fracture energy of heat-damaged cementitious specimens. Furthermore, the ANN is employed to predict the impact of several variables including; the content of polypropylene (PP) microfibers and carbon nanotubes (CNTs) used in the concrete, mortar, or paste mix design, length of PP fibers, the average diameter of CNTs, and the average length of CNTs. The influence of the studied parameters is investigated at different heating levels ranged from 25°C to 800°C. The results demonstrate that the developed ANN models have a strong potential for predicting the mechanical properties of the heated cementitious composites based on the mixing ingredients in addition to the heating conditions.