Recycled Aggregates Concrete Compressive Strength Prediction Using Artificial Neural Networks (ANNs)

Abstract

The recycled aggregate is an alternative with great potential to replace the conventional concrete alongside with other benefits such as minimising the usage of natural resources in exploitation to produce new conventional concrete. Eventually, this will lead to reducing the construction waste, carbon footprints and energy consumption. This paper aims to study the recycled aggregate concrete compressive strength using Artificial Neural Network (ANN) which has been proven to be a powerful tool for use in predicting the mechanical properties of concrete. Three different ANN models where 1 hidden layer with 50 number of neurons, 2 hidden layers with (50 10) number of neurons and 2 hidden layers (modified activation function) with (60 3) number of neurons are constructed with the aid of Levenberg-Marquardt (LM) algorithm, trained and tested using 1030 datasets collected from related literature. The 8 input parameters such as cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age are used in training the ANN models. The number of hidden layers, number of neurons and type of algorithm affect the prediction accuracy. The predicted recycled aggregates compressive strength shows the compositions of the admixtures such as binders, water–cement ratio and blast furnace–fly ash ratio greatly affect the recycled aggregates mechanical properties. The results show that the compressive strength prediction of the recycled aggregate concrete is predictable with a very high accuracy using the proposed ANN-based model. The proposed ANN-based model can be used further for optimising the proportion of waste material and other ingredients for different targets of concrete compressive strength.

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

“…It is a mixture of multiple materials like coarse aggregate, fine aggregate, water, binder, admixtures, and supplementary raw materials [13]. The concrete matrix is the random distribution of the previously mentioned variables [14]. The extensive use of it can be seen as a building material around the globe.…”

“…The extensive use of it can be seen as a building material around the globe. For the effective evaluation of the performance of concrete according to the advanced design technologies, its mechanical properties must be examined [14]. One of its supreme mechanical properties is its compressive strength, which is alternately the sign of structural safety throughout life [15].…”

“…This remarkable property of concrete can be affected by numerous factors, like particle size, water-tocement ratio, waste composition, and use of chemicals. However, casting concrete by using the proper techniques in the laboratory and conducting experimental tests to find the mechanical properties after the setting is quite a time-consuming task [14]. Moreover, using the previously mentioned technique in the recent and modern period of life is quite uneconomical.…”

Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches

“…In this study, non-linear activation algorithms such as sigmoid are utilized due to their superior reliability. The main objective of training the ANNs model is to reduce the error function, which is often known as the mean square error or MSE [55].…”

Using Multivariate Regression and ANN Models to Predict Properties of Concrete Cured under Hot Weather

“…These have also been used to estimate concrete's fc based on analyses of images captured with a digital microscope [52]. ANN models have been used to investigate the effects of recycled aggregates on concrete properties [53]. It is one of the most effective and popular techniques that has proven to be useful over time for concrete studies [14,54].…”