Artificial bee colony optimized random forest model for prediction of fly ash concrete compressive strength. [PDF]
Bali M, Mishra VP, Yenkikar A.
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This research investigated the effects of changing the cementitious content required at a given water-to-cement ratio (w/c) on workability, strength, and durability of a concrete mixture.
core
Prediction of Concrete Compressive Strength Based on ISSA-BPNN-AdaBoost. [PDF]
Li P, Zhang Z, Gu J.
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THE ANALYSIS OF THE COMPRESSION STRENGTH OF CONCRETE
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Includes bibliographical references.Since the 1980s, the use of fibre reinforced polymer (FRP) composites in strengthening and rehabilitation of existing reinforced and pre-stressed structures has gained popularity.
Ruiters, Alton
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Prediction of concrete compressive strength using a Deepforest-based model. [PDF]
Zhang W, Guo J, Ning C, Cheng R, Liu Z.
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Influence of Optimization Algorithms and Computational Complexity on Concrete Compressive Strength Prediction Machine Learning Models for Concrete Mix Design. [PDF]
Ziolkowski P.
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Machine learning and interactive GUI for concrete compressive strength prediction. [PDF]
Elshaarawy MK, Alsaadawi MM, Hamed AK.
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Evaluation of Early-Age Concrete Compressive Strength with Ultrasonic Sensors. [PDF]
Yoon H, Kim YJ, Kim HS, Kang JW, Koh HM.
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Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength. [PDF]
Alibrahim B, Habib A, Habib M.
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