首页|Study Findings from New Jersey Institute of Technology Update Knowledge in Machi ne Learning (A Machine Learning Approach to Predict Relative Residual Strengths of Recycled Aggregate Concrete after Exposure to High Temperatures)
Study Findings from New Jersey Institute of Technology Update Knowledge in Machi ne Learning (A Machine Learning Approach to Predict Relative Residual Strengths of Recycled Aggregate Concrete after Exposure to High Temperatures)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting originating from N ewark, New Jersey, by NewsRx correspondents, research stated, "In recent years, there has been a heightened focus among researchers and policymakers on assessin g the environmental impact and sustainability of human activities." The news reporters obtained a quote from the research from New Jersey Institute of Technology: "In this context, the reutilization of construction materials, pa rticularly recycled aggregate concrete, has emerged as an environmentally friend ly choice in construction projects, gaining significant traction. This study add resses the critical need to investigate the mechanical properties of recycled ag gregate concrete under diverse extreme scenarios. Conducting an extensive litera ture review, key findings were synthesized on the relative residual strength of recycled aggregate concrete following exposure to high temperatures. Leveraging these insights, innovative hybrid machine learning models were developed, offeri ng practical equations and model trees for predicting the relative residual comp ressive strength, flexural strength, elasticity modulus, and splitting tensile s trength of recycled aggregate concrete post high temperature exposure. Uncertain ty analysis was performed on each model to assess the reliability, while sensiti vity analysis was performed to find out the significance of each input variable for each predictive model. This paper presents interpretable models achieving hi gh levels of performance, with R2 values of 0.91, 0.94, 0.9, and 0.96 for predic ting the relative residual compressive strength, flexural strength, modulus of e lasticity, and splitting tensile strength of RCA concrete exposed to high temper atures, respectively."
New Jersey Institute of TechnologyNewa rkNew JerseyUnited StatesNorth and Central AmericaCyborgsEmerging Tech nologiesMachine Learning
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