Predictive analysis of brick powder-enhanced self-compacting mortar using artificial neural networks
Abstract
Abstract:
introduction/purpose: The self-compacting mortar is special mortar that has the flowability to consolidate by only the force of self-weight, with no need for any mechanical vibration, which creates its great value in application in complicated construction and repairs. In this paper, research will be conducted on using brick powder as a replacement for cement in SCM with a detailed study of effects on workability, compressive strength, and all other performance.
Methods: The proposed research models the relationship of different parameters, brick powder content, and fineness with the resultant properties of mortar using artificial neural networks (ANN) models.
results : Results showed that the addition of brick powder up to 10% cement replacement improves the workability, giving a slump flow ranging from 306 to 309 mm and funnel flow time between 4.8 and 5.4 s, while its compressive strength was ranging from 45 to 60 MPa at 28 days. Whereas in higher replacement levels than 20%, slump flow reduced to 285 mm, the time to funnel flow is increased to 9 s, and compressive strength decreased to 35 MPa.
Conclusion: The study illustrates brick powder as a promising recycled material for SCM applications not only to reduce environmental impacts but also to improve performance, although optimization of its replacement level should be taken carefully to balance workability and strength.
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