首页|Research in the Area of Machine Learning Reported from Khulna University (Optimi zation of recycled rubber self-compacting concrete: Experimental findings and ma chine learning-based evaluation)
Research in the Area of Machine Learning Reported from Khulna University (Optimi zation of recycled rubber self-compacting concrete: Experimental findings and ma chine learning-based evaluation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news reporting from Khulna University by New sRx journalists, research stated, "This research aims to assess the rheological and mechanical characteristics of Self-compacting concrete (SCC) incorporating w aste tire rubber aggregates (WRTA) as an interim substitute for coarse aggregate s. However, the standard experimental modeling approach has significant obstacle s when it comes to overcoming the nonlinearity and environmental susceptibility of concrete parts." Our news editors obtained a quote from the research from Khulna University: "The refore, linear regression (LR) and extreme gradient boosting (XGBoost) were used as two standard single machine learning (ML) models to predict the aforemention ed rubberized SCC features. In this study, conventional coarse aggregates were s upplanted with WRTA at 0%, 5%, 10%, and 2 0% to uncover the optimal proportion of coarse aggregates substitu ting rubber. To find the optimum amount of WRTA to use as a substitute, the stud y follows the impacts of rubber on the self-compacting rubberized concrete's (SC RC) rheological and mechanical characteristics. The consequences on fresh proper ties were investigated by the slump flow, J-ring, and V-funnel tests, while comp ressive and splitting tensile strengths tests were conducted to assess mechanica l properties. Increasing WRTA test outputs indicated a deterioration in workabil ity and hardened qualities. While a 10% swapping ratio is deemed f easible for producing SCRC, optimal results were achieved by reducing environmen tal impacts and efficiently managing a significant volume of rubber tire waste w ith a 5% substitution of rubber within the coarse aggregates. The research findings indicated a noticeable decrease in fresh properties as the WRT A content increased."
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