首页|Findings from Khulna University of Engineering and Technology Provides New Data on Machine Learning [High-temperature Effect On the Mechanica l Behavior of Recycled Fiber-reinforced Concrete Containing Volcanic Pumice Powd er: an Experimental ...]
Findings from Khulna University of Engineering and Technology Provides New Data on Machine Learning [High-temperature Effect On the Mechanica l Behavior of Recycled Fiber-reinforced Concrete Containing Volcanic Pumice Powd er: an Experimental ...]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news originating from Khulna, Bangladesh, by NewsRx cor respondents, research stated, “The increasing volume of waste generated by vario us activities has increased interest in using waste to create sustainable constr uction materials to achieve possible benefits. In addition, using recycled mater ials to produce fresh concrete is a desirable option because of its low cost, lo wer landfill space requirement, and the completed concrete quality.” Our news journalists obtained a quote from the research from the Khulna Universi ty of Engineering and Technology, “Therefore, an experimental inquiry is underta ken to ascertain the impacts of up to 20 wt% cement displaced by V olcanic Pumice Powder (VPP) with the incorporation of 1% and 2% Recycled Nylon Fiber (RNF) on the mechanical properties of concrete composites f ollowing room temperature to high-temperature (600 C-degrees) exposure. Fresh co ncrete characteristics tests were performed, including slump, compacting factor, Kelly ball penetration, and density. The heat resistance of the concrete was th en measured by calculating the percentage decrease in weight, the splitting tens ile strength, and the compressive strength of the specimens. Heating mainly rais ed VPP’s pozzolanic reactivity and lowered high vapor pressure through melting R NF. Therefore, VPP and RNF-treated concrete had superior mechanical performance than control concrete even when exposed to elevated temperatures. Further, the m icrostructural modifications brought on by RNF and VPP additions were also explo red by deploying Scanning Electron Microscopy (SEM). The use of VPP in concrete led to an improvement in fresh properties, while RNF demonstrated deterioration in the same qualities. Despite this, supervised machine learning techniques are a central focus of this investigation because of their potential to predict conc rete characteristics accurately. To predict the fresh and mechanical characteris tics of concrete, both the Random Forest (RF) and the K-Nearest Neighbors (KNN) algorithm, along with their ensemble model counterparts, were explored. The outc omes revealed that RNF and VPP considerably improved the concrete’s heat resilie nce and mechanical characteristics and halted the concrete composites’ explosive spalling behavior at 600 C-degrees temperatures. To prevent strength loss at hi gh temperatures, it was discovered that adding 1 % RNF content to c oncrete with 10% VPP was the best combination.”
KhulnaBangladeshAsiaCyborgsEmerg ing TechnologiesMachine LearningKhulna University of Engineering and Technol ogy
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