Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural network

Abstract

In this study, an artificial neural network model for compressive strength of self-compacting concretes (SCCs) containing mineral additives and polypropylene (PP) fiber exposed to elevated temperature were devised. Portland cement (PC) was replaced with mineral additives such as fly ash (FA), granulated blast furnace slag (GBFS), zeolite (Z), limestone powder (LP), basalt powder (BP) and marble powder (MP) in various proportioning rates with and without PP fibers. SCC mixtures were prepared with water to powder ratio of 0.33 and polypropylene fibers content was 2 kg/m 3 for the mixtures containing polypropylene fibers. Specimens were heated up to elevated temperatures (200, 400, 600 and 800 °C) at the age of 56 days. Then, tests were conducted to determine loss in compressive strength. The results showed that a severe strength loss was observed for all of the concretes after exposure to 600 °C, particularly the concretes containing polypropylene fibers though they reduce and eliminate the risk of the explosive spalling. Furthermore, based on the experimental results, an artificial neural network (ANN) model-based explicit formulation was proposed to predict the loss in compressive strength of SCC which is expressed in terms of amount of cement, amount of mineral additives, amount of aggregates, heating degree and with or without PP fibers. Besides, it was found that the empirical model developed by using ANN seemed to have a high prediction capability of the loss in compressive strength of self compacting concrete (SCC) mixtures after being exposed to elevated temperature.