首页|Duy Tan University Researcher Reports Recent Findings in Machine Learning (Lever aging a Hybrid Machine Learning Approach for Compressive Strength Estimation of Roller-Compacted Concrete with Recycled Aggregates)
Duy Tan University Researcher Reports Recent Findings in Machine Learning (Lever aging a Hybrid Machine Learning Approach for Compressive Strength Estimation of Roller-Compacted Concrete with Recycled Aggregates)
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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 Da Nang, Vietnam, by New sRx journalists, research stated, "In recent years, the use of recycled aggregat e (RA) in roller-compacted concrete (RCC) for pavement construction has been inc reasingly attractive due to various environmental and economic benefits. Early d etermination of the compressive strength (CS) is crucial for the construction an d maintenance of pavement." The news editors obtained a quote from the research from Duy Tan University: "Th is paper presents the idea of combining metaheuristics and an advanced gradient boosting regressor for estimating the compressive strength of roller-compacted c oncrete containing RA. A dataset, including 270 samples, has been collected from previous experimental works. Recycled aggregates of construction demolition was te, reclaimed asphalt pavement, and industrial slag waste are considered in this dataset. The extreme gradient boosting machine (XGBoost) is employed to general ize a functional mapping between the CS and its influencing factors. A recently proposed gradient-based optimizer (GBO) is used to fine-tune the training phase of XGBoost in a data-driven manner. Experimental results show that the hybrid GB O-XGBoost model achieves outstanding prediction accuracy with a root mean square error of 2.64 and a mean absolute percentage error less than 8%. T he proposed method is capable of explaining up to 94% of the variation in the CS."
Duy Tan UniversityDa NangVietnamCyborgsEmerging TechnologiesMachine Learning
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