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2 result(s) for "Praburanganathan, S."
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Force-Deformation Study on Glass Fiber Reinforced Concrete Slab Incorporating Waste Paper
This study inspects the viability of engaging the discarded paper wastes in concrete by varying the volume proportions from 0%–20% with each 5% increment in replacement of the weight of cement. A physiomechanical study was conducted, and the results were presented. A glass fiber reinforced rectangular slab with a longer span (ly) to shorter span (lx) ratio of (ly: lx) 1.16 was cast with optimum replacement of waste-paper mass and compared the force-deformation characteristics with the conventional concrete slab without waste paper. The optimum percentage of discarded papers for the replacement of cement is 5%. Also, the results imply that the compressive strength at the age of 28 days is 30% improved for the optimum replacement. Based on the outcomes of the investigation, it can be inferred that the compressive strength gets progressively reduced if the volume of the discarded paper gets increases. The incorporation of glass fibers improves the split and flexural strength of the concrete specimens considerably. The ultimate load-carrying capacity of the glass fiber reinforced waste paper incorporated concrete slab measured 42% lower than that of the conventional slab. However, development of the new type of concrete incorporating waste papers is the new trend in ensuring the sustainability of construction materials.
Synergistic effect on the performance of ash-based bricks with glass wastes and granite tailings along with strength prediction by adopting machine learning approach
The study proposes a novel and sustainable method to appropriately utilize wastes from granite as well as glass industries in brick manufacturing. An ecofriendly and low-cost manufacturing process of ash-based bricks pertaining to the Indian standard codal provisions that can be adopted on the commercial scale is deliberated. The research also recommends the method for predicting the strength of the ash-based bricks using machine learning algorithms like random forests and decision trees. For positive synergy in the performance, both the granite tailings and glass waste must be used together. Using the granite tailings and glass waste together led to a significant reduction of 75% in the fly ash requirement without compromising the brick’s performance. The addition of the granite tailings and glass waste in the mix could increase the strength of the brick by 90.5% and 37.7%, respectively. Beyond 30% dosage of granite, tailings are not recommended as they may lead to the poor gradation of particles and weak bonding in the microstructure. The glass waste in the mixture should not be more than 15% as it causes the dilution of pozzolanic reactions thereby forming fewer hydrated compounds. Brick’s durability is known after exposing the specimens for 1 year to sewers and biogenic corrosion environment, marine environment, and saline soil environment, respectively. The inclusion of the industrial wastes significantly reduced the specimen damage in the extreme environmental conditions along with the least absorption rates. The dosage of ash, granite tailings, and glass waste has to be maintained around 15%, 30%, and 15%, respectively for attaining the optimum performance. Out of the generated machine learning algorithms, only random forests could be able to predict the values accurately with R 2 values at 0.90 and with comparatively lesser errors.