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%.
Highlights
There is no doubt that the utilization of recycled aggregate concrete (RAC) has been the best way to resolve the problem of the increasing amount of construction and demolition (C&D) waste and further attain sustainable development
When each one of the eight aggregate characteristics was added to the input variables alone, the results showed that three characteristics could not improve the prediction, while the other five characteristics could help to optimize the model; among the eight aggregate characteristics, the SGSSD and water absorption value (Wa) played the most significant influence, being capable of reducing the error to about 4.84% and 4.83%, respectively
The purpose of this paper was to analyze the significance of each aggregate characteristic and determine the best combinations of factors which further influence the compressive strength and elastic modulus of RAC using the artificial neural networks (ANN) model
Summary
There is no doubt that the utilization of recycled aggregate concrete (RAC) has been the best way to resolve the problem of the increasing amount of construction and demolition (C&D) waste and further attain sustainable development. A previous study [18] used regression analysis to propose a number of equations relating the hardened properties (compressive strength) of RAC with the water absorption or density of different types and combinations of aggregates obtained from different sources. Duan et al [20] carried out a study on predicting the compressive strength of RAC at the curing time of 28 days using an ANN model. In theory, related to the properties of the old mortar attached, which can affect the properties of RAC by the more factors are taken into consideration, the more accurate the model is. How to fully represent the aggregate properties in the ANN model is applicable to the majority of RAs from different sources. The following steps were used for this purpose
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