Prediction of Mechanical Properties of Rubberized Concrete Incorporating Fly Ash and Nano Silica by Artificial Neural Network Technique

Abstract

The use of enormous amounts of material is required for production. Due to the current emphasis on the environment and sustainability of materials, waste products and by-products, including silica fume and fly ash (FA), are incorporated into concrete as a substitute partially for cement. Additionally, concrete fine aggregate has indeed been largely replaced by waste materials like crumb rubber (CR), thus it reduces the mechanical properties but improved some other properties of the concrete. To decrease the detrimental effects of the CR, concrete is therefore enhanced with nanomaterials such nano silica (NS). The concrete mechanical properties are essential for the designing and constRuction of concrete structures. Concrete with several variables can have its mechanical characteristics predicted by an artificial neural network (ANN) technique. Using ANN approaches, this paper predict the mechanical characteristics of concrete constructed with FA as a partial substitute for cement, CR as a partial replacement for fine aggregate, and NS as an addition. Using an artificial neural network (ANN) technique, the mechanical characteristics investigated comprise splitting tensile strength (Fs), compressive strength (Fc), modulus of elasticity (Ec) and flexural strength (Ff). The ANN model was used to train and test the dataset obtained from the experimental program. Fc, Fs, Ff and Ec were predicted from added admixtures such as CR, NS, FA and curing age (P). The modelling result indicated that ANN predicted the strength with high accuracy. The proportional deviation mean (MoD) values calculated for Fc, Fs, Ff and Ec values were −0.28%, 0.14%, 0.87% and 1.17%, respectively, which are closed to zero line. The resulting ANN model’s mean square error (MSE) values and coefficient of determination (R2) are 6.45 × 10−2 and 0.99496, respectively.

An Intelligent Approach for Predicting Mechanical Properties of High-Volume Fly Ash (HVFA) Concrete

“…The parameters with relatively lower accuracy in comparison with Fc and Ff are γf and $. Several studies have also reported the suitability of a machine learning approach for the prediction of concrete strength with high accuracy and stability [36,37] Examination of MoD values gives sufficient information about the error rates of the ANN model. However, in order to make the prediction error analysis of the ANN model more comprehensive, it is important to examine the target values for each data point and the prediction values obtained from the ANN model.…”

Prediction of the Compressive Strength of Vibrocentrifuged Concrete Using Machine Learning Methods

“…ANNs are also actively used to predict various properties of concrete made using various modifying additives, fiber fibers, lightweight and recycled aggregates, and the developed models show fairly accurate results [36][37][38][39][40][41].…”

Prediction of Tool Eccentricity Effects on the Mechanical Properties of Friction Stir Welded AA5754-H24 Aluminum Alloy Using ANN Model

“…Through years of experiments and research, ANN techniques have demonstrated robust learning capabilities and high prediction accuracy through its ability to identify complex mathematical relationships between input and output variables [ 20 ]. Accordingly, ANN models have been widely used in the literature as predictive techniques in a range of different fields, including healthcare [ 21 , 22 ], sustainable development [ 18 , 19 , 23 ], agriculture [ 23 , 24 ], and material science [ 25 , 26 , 27 , 28 ].…”