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Advancing Compressive Strength Prediction in Self-Compacting Concrete via Soft Computing: A Robust Modeling Approach

Ghorbani, A ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.22115/SCCE.2023.396669.1646
  3. Publisher: 2024
  4. Abstract:
  5. Self-Compacting Concrete (SCC) is a unique type of concrete that can flow and fill spaces without the need for vibrating compaction, resulting in a dense and uniform material. This article focuses on estimating the compressive strength of SCC utilizing Artificial Neural Networks. Specifically, the study employs multilayer perceptrons with back-propagation learning algorithms, which are commonly used in various problem-solving scenarios. The study covers essential components such as structure, algorithm, data preprocessing, over-fitting prevention, and sensitivity analysis in MLPs. The input variables considered in the research include water, fine aggregate, super-plasticizer, fly ash, coarse aggregate, ground granulated blast furnace slag, limestone powder, viscosity-modifying admixtures, cement, silica fume, and rice husk ash. The target variable is the compressive strength. Through a sensitivity analysis, the study evaluates the relative importance of each parameter. The results indicate that the AI-based model accurately predicts the compressive strength of self-compacting concrete. © 2023 The Authors
  6. Keywords:
  7. Artificial neural network ; Compressive strength prediction ; Robust approach ; Self-compacting concrete ; Sensitivity analysis
  8. Source: Journal of Soft Computing in Civil Engineering ; Volume 8, Issue 1 , 2024 , Pages 126-140 ; 25882872 (ISSN)
  9. URL: https://www.jsoftcivil.com/article_172919.html