首页|University of Rhode Island Reports Findings in Machine Learning (Integrating Dat a Imputation and Augmentation With Interpretable Machine Learning for Efficient Strength Prediction of Fly Ash-based Alkali-activated Concretes)

University of Rhode Island Reports Findings in Machine Learning (Integrating Dat a Imputation and Augmentation With Interpretable Machine Learning for Efficient Strength Prediction of Fly Ash-based Alkali-activated Concretes)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Fresh data on Machine Learning are pre sented in a new report. According to newsreporting originating from Kingston, R hode Island, by NewsRx correspondents, research stated, “Flyash-based alkali-ac tivated concrete (AAC) is renowned for its superior mechanical performance and sustainability, presenting an attractive alternative to traditional Portland ceme nt concrete. Despite theseadvantages, the broad compositional range of AACs pre sents challenges in precisely tailoring materialproperties.”

KingstonRhode IslandUnited StatesN orth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniv ersity of Rhode Island

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Dec.17)