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Estimation of the Compressive Strength of GGBFS based Alkali-Activated Concrete

Jafari, Alireza | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54143 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Toufigh, Vahab
  7. Abstract:
  8. Ground granulated blast furnace slag (GGBFS) based alkali-activated concrete (GAAC) is one of the eco-friendly alternatives of traditional concrete, which uses industrial byproducts instead of ordinary Portland cement (OPC). Besides environmental benefits, substituting OPC concrete with GAAC has some economic advantages, while it successfully meets the concrete structural requirements in both mechanical and durability characteristics. Despite this fact, employing GAAC in the construction industry has been limited due to a lack of standard mix design code.This study aims to develop an accurate prediction model for the compressive strength of GAAC by employing the most appropriate machine learning technique and evaluate the sustainability index of GAAC based on carbon dioxide emission. In the first phase, novel mix design parameters based on analyzing 162 collected mixes were proposed. These parameters demonstrate the amount and chemical composition of fly ash-based geopolymer concrete (FAGC) constituents. Existing numerous data and nature similarity between GAAC and FAGC are the reason for considering fly ash-based geopolymer concrete data in the first phase of this study. This phase also proposes a novel accurate prediction model for the compressive strength of FAGC. The second phase examines and compares the effect of the first phase proposed mix design parameters with the traditional ones on the compressive strength of GAAC based on tested specimens. In the first step, the effect of aggregate content on the compressive strength of GAAC was examined according to 5 tested mixes. In the next step, the results of the first step were used to design and construct 124 mixes prepared in 620 cubic specimens. The compressive strength of these mixes was then measured and the data were used to assess the effect of various mix designs and their parameters on the compressive strength of GAAC. The results showed that the optimum aggregate to total mix mass ratio is 0.73. Findings also demonstrated the superiority of the chemical composition mix design method and its parameters in estimating the compressive strength of GAAC. The third phase of this study focuses on developing a high-performance prediction model by the most appropriate machine learning algorithm. Numerous neural networks were examined in this phase to determining the most appropriate neural network parameters that can predict the compressive strength of GAAC. At the end of this phase, an accurate prediction model was proposed based on the obtained data and results in the second phase. In the last phase of this study, the CO2 emissions of GAAC, FAGC, and OPC concrete with a compressive strength of 35 MPa were evaluated. The results illustrated that utilizing GAAC can reduce the CO2 emission of OPC concrete and FAGC by 33 and 9 percent, respectively. Findings also showed the important role of sodium-based alkaline solutions in the CO2 emissions of GAAC and FAGC, more than 60 percent, which necessitate their replacement with more sustainable solutions
  9. Keywords:
  10. Geopolymer Concrete ; Mix Design ; Chemical Compounds ; Neural Network ; Compression Strength ; Carbon Footprint Reduction ; Parametrics Evaluation ; Alkali-Activated Concrete

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