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HPASC – OPCC bi-surface Shear Strength Prediction Model Using Deep Learning

Khademi, Pooria | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54975 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Toufigh, Vahab
  7. Abstract:
  8. Selecting a suitable material is crucial for repairing the old concrete structures and joining precast panels of bridges, especially the bond strength between the substrate concrete and the overlay material. In this regard, this research focused on high-performance alkali-activated slag concrete (HPASC) as a new concrete used as an overlay on ordinary Portland cement concrete (OPCC) as a block of old concrete. Approximately four hundred bi-surface shear (BSS) tests were performed to evaluate the interface properties of OPCC and HPASC. HPASC specimens were designed with different NaOH molarity, silica fume (SF) content, steel fiber content, age of repair material, and proportion of grooved surface area to the gross area. The results indicated that the steel fibers in HPASC improved the compressive strength and increased the BSS strength. The convolutional neural network (CNN) and multilayered perceptron (MLP) were used to determine a comprehensive model for simulating the effect of each parameter on the BSS shear strength. The simulation results suggest that the CNN, as compared to MLP, can more accurately predict experimental data due to the less MSE and higher R-factor. According to the conducted simulations, HPASC mixed with 2% fibers, 12 molars NaOH solution, 20% SF content, and 30% grooved surface was the most suitable mixture for repair and rehabilitation.
  9. Keywords:
  10. Convolutional Neural Network ; Ultrasonic Test ; Perceptron Neural Network ; Multi-Layer Perceptron (MLP) ; Recycled Fibre ; Steel Fibers ; Geopolymer Concrete ; High Strength Alkali-Activated Slag Concrete ; Bi-Surface Shear Test

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