Using Neural Networks to Determine the Significance of Aggregate Characteristics Affecting the Mechanical Properties of Recycled Aggregate Concrete

Abstract

It has been proved that artificial neural networks (ANN) can be used to predict the compressive strength and elastic modulus of recycled aggregate concrete (RAC) made with recycled aggregates from different sources. This paper is a further study of the use of ANN to analyze the significance of each aggregate characteristic and determine the best combinations of factors that would affect the compressive strength and elastic modulus of RAC. The experiments were carried out with 46 mixes with several types of recycled aggregates. The experimental results were used to build ANN models for compressive strength and elastic modulus, respectively. Different combinations of factors were selected as input variables until the minimum error was reached. The results show that water absorption has the most important effect on aggregate characteristics, further affecting the compressive strength of RAC, and that combined factors including concrete mixes, curing age, specific gravity, water absorption and impurity content can reduce the prediction error of ANN to 5.43%. Moreover, for elastic modulus, water absorption and specific gravity are the most influential, and the network error with a combination of mixes, curing age, specific gravity and water absorption is only 3.89%.

Experimental and Theoretical Prediction Model Research on Concrete Elastic Modulus Influenced by Aggregate Gradation and Porosity

“…On the other hand, the differential self-consistent (DSC) method can give accurate predictions for a matrix with high porosity, which is more suitable to our research condition [29]. Therefore, in this current research it is proposed to adopt the DSC method to determine the effective bulk modulus and shear modulus of the equivalent ITZ, which could be discovered in Equations (6)- (8).…”

“…As one of the important parameters characterizing the macro mechanical properties of concrete, elastic modulus is also an important parameter in the study of porous materials, and many prediction models have been developed in earlier studies [6][7][8].…”

Importance of artificial neural networks in civil engineering: a systematic review of the literature

“…Se ha encontrado un estudio que aplicó un Perceptrón Multicapa para predecir la resistencia a la compresión y el módulo elástico de concretos hechos con agregados reciclados [14]. En este estudio se analiza la importancia de las características de los agregados reciclados para ver cuáles influyen más en la resistencia a la compresión y el módulo elástico del concreto.…”

Special Issue: New Trends in Recycled Aggregate Concrete

“…Duan et al [11] used artificial neural networks (ANNs) to determine the significance of aggregate characteristics on the mechanical properties of recycled aggregate concrete (RAC). The results show that water absorption has the most important effect on aggregate characteristics, further affecting the compressive strength of RAC, and that combined factors including concrete mixes, curing age, specific gravity, water absorption, and impurity content can reduce the prediction error of ANNs to 5.43%.…”