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Performance evaluation of slag-based concrete at elevated temperatures by a novel machine learning approach

Toufigh, V ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1016/j.conbuildmat.2022.129357
  3. Publisher: Elsevier Ltd , 2022
  4. Abstract:
  5. Ground granulated blast furnace slag is a sustainable material and supplementary for cement in the concrete industry. Different behavioral aspects must be assessed to achieve reliable sustainable materials, including post-fire mechanical properties. One robust tool is the machine learning approach to train prediction models. This study proposes a novel machine learning algorithm, hybrid support vector regression and dolphin echolocation algorithm (SVR-DE), to predict the post-fire compressive strength ratio of slag-based concrete. In this regard, SVR hyper-parameters were tuned by the DE optimization algorithm. Four kernel functions were implemented in SVR formulation: linear, sigmoid, polynomial, and RBF. Accordingly, four different models were proposed based on the defined kernels with comparing their accuracy. The training and testing process was applied to 80% and 20% of 124 collected data from relevant studies in the literature, respectively. Although the models with sigmoid and RBF kernel functions yielded a satisfactory fit, both with 0.86 R2 value, the model with polynomial kernel function expressed high accurate results with R2 and RMSE of 0.92 and 0.59. A sensitivity analysis was performed eventually to investigate the importance of input parameters. Six parameters displayed a high importance class in the post-fire strength ratio of slag-based concrete, including fine/coarse aggregate, water/cement, slag/water, slag/superplasticizer, slag/cement, and temperature. © 2022 Elsevier Ltd
  6. Keywords:
  7. Dolphin Echolocation Optimization Algorithm ; Machine Learning ; Post-fire ; Slag ; Support Vector Regression ; Sustainable Materials ; Animals ; Biomimetics ; Blast furnaces ; Compressive strength ; Concrete aggregates ; Concrete mixtures ; Curing ; Dolphins (structures) ; Learning algorithms ; Optimization ; Sensitivity analysis ; Elevated temperature ; Kernel function ; Machine learning approaches ; Machine-learning ; Optimization algorithms ; Performances evaluation ; Support vector regressions ; Slags
  8. Source: Construction and Building Materials ; Volume 358 , 2022 ; 09500618 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0950061822030136