Estimation of compressive strength of concrete cement using random vector functional link networks: a case study
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-023-08885-4.pdf
Reference45 articles.
1. Akbari M, JafariDeligani V (2020) Data driven models for compressive strength prediction of concrete at high temperatures. Front Struct Civ Eng 14(2):311–321
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3. Asteris PG, Roussis PC, Douvika MG (2017) Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors (switzerland) 17(6):1344
4. Asteris PG, Moropoulou A, Skentou AD, Apostolopoulou M, Mohebkhah A, Cavaleri L, Rodrigues H, Varum H (2019) Stochastic vulnerabilityassessment of masonry structures: concepts, modeling and restoration aspects. Appl Sci 9(2):243
5. Asteris PG, Mokos VG (2019) Concrete compressive strength using artificial neural networks. Neural Comput Appl pp. 1–20
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