The use of recycled aggregate concrete (RAC) in the construction industry can help to prevent irreparable environmental damages and to mitigate the depletion rate of natural resources. However, the quality of the RAC ...
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The use of recycled aggregate concrete (RAC) in the construction industry can help to prevent irreparable environmental damages and to mitigate the depletion rate of natural resources. However, the quality of the RAC should be investigated before its practical applications. Compressive strength of the RAC (f ' RAC) is one of the most crucial design parameters, which is measured by time-consuming and cost-extensive experiments. One solution to restrict the number of experiments and achieve reliable f ' RAC estimation is through employing machine learning methods, Artificial Bee Colony Expression Programming (ABCEP) is a newly proposed automaticregression technique that is used in this study to predict the f ' RAC. For comparison purposes, four extensions of artificial bee colony programming techniques (i.e., Artificial Bee Colony Programming (ABCP), quick Artificial Bee Colony Programming (qABCP), Quick semantic Artificial Bee Colony Programming (qsABCP), and Semantic Artificial Bee Colony Programming (sABCP)) were also served. To analyze the results, the average and best performances of all algorithms, regression analysis, execute run times, Wilcoxon signed-rank test, and the behavior of algorithms dealing with the local optima were investigated. The results show that the ABCEP method is the most effective technique, with the average root mean squared error of 10.36 MPa compared to 16.23 MPa, 10.82 MPa, 17.71 MPa, and 14.20 MPa for the developed ABCP-, qABCP-, qsABCP-, and sABCP-based models, respectively, in colony size of 30. In addition, the run time of this algorithm is remarkably less than the other algorithms.(c) 2022 Elsevier B.V. All rights reserved.
Waste from concrete demolition is a sustainability concern that can be mitigated when used as recycled aggregate in concrete instead of virgin natural aggregates. However, the durability of recycled aggregate con-cret...
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Waste from concrete demolition is a sustainability concern that can be mitigated when used as recycled aggregate in concrete instead of virgin natural aggregates. However, the durability of recycled aggregate con-crete (RAC), including concrete carbonation, needs to be investigated before the widespread applications of RAs in construction. Developing artificial intelligence-based predictive models for estimating the carbonation depth of RAC using the available data can reduce the need for experimental studies to generate reliable models for the service life assessment of concrete structures. In this study, artificial bee colony expression programming (ABCEP), as a novel branch of automaticregression technique, was used to predict the carbonation depth of RAC from a large dataset consisting of 655 data samples. Several ABCEP architectures were developed, different analyses were conducted, and a comparison study between the best ABCEP model and previous models published in the literature was conducted. The findings show that the best structure of the ABCEP model could estimate the carbonation depth of RAC with a reasonable root mean square error of 3.33 mm. The exposure time was the most influential parameter affecting the carbonation depth of RAC. Furthermore, the ABCEP model could outperform the previous models, despite the larger unknown dataset used to test its performance.
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