A hybrid intelligent system for designing optimal proportions of recycled aggregate concrete

Document Type

Journal Article

Publication Title

Journal of Cleaner Production

ISSN

09596526

Volume

273

Publisher

Elsevier

School

School of Engineering

RAS ID

32131

Funders

China Scholarship Council

Comments

Zhang, J., Huang, Y., Aslani, F., Ma, G., & Nener, B. (2020). A hybrid intelligent system for designing optimal proportions of recycled aggregate concrete. Journal of Cleaner Production, 273, article 122922. https://doi.org/10.1016/j.jclepro.2020.122922

Abstract

© 2020 Elsevier Ltd The replacement of natural coarse aggregate (NCA) with recycled coarse aggregate (RCA) in concrete mixtures offers various advantages, including conservation of natural resources, reduction of CO2 emissions, and cost reduction. However, multiple related variables and objectives (e.g., mechanical, economic, and environmental objectives) need to be considered when optimizing mixtures of recycled aggregate concrete (RAC). This cannot be achieved through traditional laboratory- or statistics-based methods. This study proposes a hybrid intelligent system based on artificial intelligence (AI) and metaheuristic algorithms for designing optimal mixtures of RAC. To verify the proposed model, a data set containing 344 different RAC mixtures was collected from previous literature. A semi-supervised cotraining algorithm using two k-nearest neighbor (kNN) regressors with different distance metrics is developed to label the unlabeled data in the collected dataset. Different AI models are incorporated into the system for modeling the relationship between RAC strength and its influencing variables. A multi-objective optimization (MOO) model based on AI algorithms and on a multi-objective firefly algorithm is used to search for optimal mixtures of RAC. The results show that kNN-based semi-supervised cotraining can effectively exploit unlabeled data to improve the regression estimates. In the test set, A Random Forest and Backpropagation Neural Network achieve the best prediction accuracy for predicting, respectively, uniaxial compressive strength and splitting tensile strength of RAC, indicated by the highest correlation coefficients (0.9064 and 0.8387, respectively) and lowest root-mean-square errors (6.639 MPa and 0.5119 MPa, respectively). The Pareto fronts of the multi-objective mixture optimization problem are successfully obtained by the MOO model. The proposed system can also be used to optimize mixture proportions of other cementitious materials in civil engineering.

DOI

10.1016/j.jclepro.2020.122922

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