Compressive Strength Estimation of Rice Husk Ash-Blended Concrete Using Deep Neural Network Regression with an Asymmetric Loss Function
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s40996-022-01015-4.pdf
Reference78 articles.
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3. Asadi Shamsabadi E, Roshan N, Hadigheh SA, Nehdi ML, Khodabakhshian A, Ghalehnovi M (2022) Machine learning-based compressive strength modelling of concrete incorporating waste marble powder. Constr Build Mater 324:126592. https://doi.org/10.1016/j.conbuildmat.2022.126592
4. Asghari V, Leung YF, Hsu S-C (2020) Deep neural network based framework for complex correlations in engineering metrics. Adv Eng Inform 44:101058. https://doi.org/10.1016/j.aei.2020.101058
5. Aslam F et al (2022) Compressive strength prediction of rice husk ash using multiphysics genetic expression programming. Ain Shams Eng J 13:101593. https://doi.org/10.1016/j.asej.2021.09.020
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