Compressive strength and elastic modulus of RBAC: An analysis of existing data and an artificial intelligence based prediction
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- @Article{LIN:2023:cscm,
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author = "Lang Lin and Jinjun Xu and Jialiang Yuan and Yong Yu",
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title = "Compressive strength and elastic modulus of {RBAC:} An
analysis of existing data and an artificial
intelligence based prediction",
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journal = "Case Studies in Construction Materials",
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volume = "18",
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pages = "e02184",
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year = "2023",
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ISSN = "2214-5095",
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DOI = "doi:10.1016/j.cscm.2023.e02184",
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URL = "https://www.sciencedirect.com/science/article/pii/S2214509523003649",
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keywords = "genetic algorithms, genetic programming, Recycled
brick aggregate concrete (RBAC), Compressive strength,
Elastic modulus, Artificial neural network, ANN,
Multigene genetic programming",
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abstract = "In recent years crushing waste brick to produce
recycled brick aggregates (RBAs) has become a viable
solution for reducing environmental pollution and
addressing the natural resource shortage in civil
engineering. To promote the widespread use of the
recycled brick aggregate concrete (RBAC) in
construction, this study analyzes existing test results
on the attributes of RBAs and the compressive
mechanical behaviors of RBAC. The review results
indicate significant differences and variabilities in
the characteristics of RBAs compared to natural coarse
aggregates and recycled concrete coarse aggregates.
RBAs have the highest absorption capacity and crushing
index among the three aggregates, leading to changes in
the compressive failure mechanism and a decline in the
mechanical properties of RBAC. Additionally, it is also
observed that existing formulas do not adequately
account for the deterioration of the compressive
mechanical properties of RBAC. To tackle this problem,
artificial intelligence (AI) approaches including
artificial neural network and multigene genetic
programming are used to develop precise models for
predicting the compressive strength and elastic modulus
of RBAC. It is found that RBAC's these two mechanical
indexes are mainly influenced by the standard strength
of cement paste, water-to-cement ratio,
sand-to-aggregate mass ratio, RBA replacement ratio and
mass-weighted water absorption ratio of coarse
aggregates. The AI models developed in this study
accurately capture the trends of these factors and
offer desirable predictive results",
- }
Genetic Programming entries for
Lang Lin
Jinjun Xu
Jialiang Yuan
Yong Yu
Citations