RESEARCH ARTICLE Predicting the strength properties of slurry in fi ltrated fibrous concrete using artificial neural network T. Chandra Sekhara REDDY * Civil Engineering, G.P.R. Engineering College, Kurnool 518002, Andhra Pradesh, India * Corresponding author. E-mail: tcsreddy61@gmail.com © Higher Education Press and Springer-Verlag GmbH Germany 2017 ABSTRACT This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of SIFCON containing different minerals admixture. The investigations were done on 84 SIFCON mixes, and specimens were cast and tested after 28 days curing. The obtained experimental data are trained using ANN which consists of 4 input parameters like Percentage of fiber (PF), Aspect Ratio (AR), Type of admixture (TA) and Percentage of admixture (PA). The corresponding output parameters are compressive strength, tensile strength and flexural strength. The predicted values obtained using ANN show a good correlation between the experimental data. The performance of the 4-14-3 architecture was better than other architectures. It is concluded that ANN is a highly powerful tool suitable for assessing the strength characteristics of SIFCON. KEYWORDS artificial neural networks, root mean square error, SIFCON, silica fume, metakaolin, steel fiber 1 Introduction Slurry infiltrated fibrous concrete (SIFCON) is a special type of fiber-reinforced composite material containing 20% of steel fibers [1–4]. The fiber volume in the conventional fiber reinforced concrete (FRC) is limited to about 2%, as its excess can lead to difficulty in mixing and placing of the concrete. Hence, the need to devise a different construction technique for increasing the fiber volume fraction led to the development of SIFCON [5,6]. The initial step in the preparation of SIFCON, a preplaced fiber concrete, is the placement of fibers in the form of mold. Under light external vibration, fiber placement can be accomplished either by hand or through the use of commercial fiber dispersing units. On comple- tion of fiber placement, the fine-grained cement-based slurry is poured over the pre-packed fiber bed with subsequent infiltration of the slurry aided by external vibration. High water-reducing admixture is used to provide a suitable slurry viscosity while maintaining its low water-cement (W/C) ratio. Then it is followed by the curing of SIFCON as done for other concrete materials [6]. It is essential to study the behavior of SIFCON produced with low tensile strength steel wire fibers in compression, tension, and flexure. Besides, the utility of mineral admixture like silica fume and metakaolin in producing SIFCON also needs detailed investigation. The present work, therefore, focuses on determining the methods involved in producing SIFCON using locally available low tensile strength steel wire fibers. Investigating the strength parameters of SIFCON by experimentation is time consuming, expensive and involves sampling problems. Moreover, it is difficult to develop an empirical or analytical model for SIFCON due to the complex multi parametric relationship among the various constituent materials like cement, sand, admixture, fiber, and water. Thus, in the present work, artificial neural networks (ANNs) are used for developing a compact model to predict the strength characteristics of SIFCON. 1.1 Artificial neural networks ANNs have been used in the past for modeling material characteristics. Ghaboussi et al. [7] demonstrated the application of ANN for modeling the stress-strain behavior of concrete, and Mukherjee et al. [8] presented the ANN model as a technique to enhance the mechanical behavior of metal matrix composites. Chopra et al. [9] developed Article history: Received Jan 27, 2017; Accepted Jun 3, 2017 Front. Struct. Civ. Eng. 2018, 12(4): 490–503 https://doi.org/10.1007/s11709-017-0445-3