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Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests
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Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests

Asteris, Panagiotis G. ; Skentou, Athanasia D. ; Bardhan, Abidhan ; Samui, Pijush ; Lourenço, Paulo B.

Construction & building materials, 2021-10, Vol.303, p.124450, Article 124450 [Periódico revisado por pares]

Elsevier Ltd

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Compressive strength assessment of recycled aggregate concrete using Schmidt rebound hammer and core testing
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Compressive strength assessment of recycled aggregate concrete using Schmidt rebound hammer and core testing

Kazemi, Mostafa ; Madandoust, Rahmat ; Brito, Jorge de

Construction & building materials, 2019-11, Vol.224, p.630-638 [Periódico revisado por pares]

Elsevier Ltd

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Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm
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Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm

Huang, Jiandong ; Sun, Yuantian ; Zhang, Junfei

Engineering with computers, 2022-08, Vol.38 (4), p.3151-3168 [Periódico revisado por pares]

London: Springer London

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4
Utilization of sawdust on the compressive strength of concrete with variations of cement
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Utilization of sawdust on the compressive strength of concrete with variations of cement

Darwis, Zulmahdi ; Kuncoro, Hendrian Budi Bagus ; Delpiero, Axel Xavier

Teknika, 2022-06, Vol.18 (1), p.1-6 [Periódico revisado por pares]

Universitas Sultan Ageng Tirtayasa

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5
A computational model for thick concrete slab demolition using soundless chemical demolition agent
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A computational model for thick concrete slab demolition using soundless chemical demolition agent

Jiang, Zhisheng ; Zheng, Wenzhong ; Li, Ruisen ; Guo, Changshun

Construction & building materials, 2021-10, Vol.303, p.124430, Article 124430 [Periódico revisado por pares]

Elsevier Ltd

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6
Application of Multivariate Adaptive Regression Splines (MARS) approach in prediction of compressive strength of eco-friendly concrete
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Application of Multivariate Adaptive Regression Splines (MARS) approach in prediction of compressive strength of eco-friendly concrete

Naser, Ali H. ; Badr, Ali H. ; Henedy, Sadiq N. ; Ostrowski, Krzysztof Adam ; Imran, Hamza

Case Studies in Construction Materials, 2022-12, Vol.17, p.e01262, Article e01262 [Periódico revisado por pares]

Elsevier Ltd

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7
Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples
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Applying a meta-heuristic algorithm to predict and optimize compressive strength of concrete samples

Sun, Lei ; Koopialipoor, Mohammadreza ; Jahed Armaghani, Danial ; Tarinejad, Reza ; Tahir, M. M.

Engineering with computers, 2021-04, Vol.37 (2), p.1133-1145 [Periódico revisado por pares]

London: Springer London

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8
Shear and flexural strengthening of reinforced concrete beams with variable compressive strength values using externally bonded carbon fiber plates
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Shear and flexural strengthening of reinforced concrete beams with variable compressive strength values using externally bonded carbon fiber plates

Al-Shamayleh, Rawan ; Al-Saoud, Huda ; Abdel-Jaber, Mu'tasim ; Alqam, Maha

Results in engineering, 2022-06, Vol.14, p.100427, Article 100427 [Periódico revisado por pares]

Elsevier B.V

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9
Parametric Study for Behavior of the Punching Shear Capacity of Flat Slab Without Shear Reinforcement by The Selected Design Codes
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Parametric Study for Behavior of the Punching Shear Capacity of Flat Slab Without Shear Reinforcement by The Selected Design Codes

Hawar Hasan Jasim

Eurasian journal of science and engineering (Online), 2023-02, Vol.9 (1), p.57-70 [Periódico revisado por pares]

Tishk International University

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10
An ANN Model for Predicting the Compressive Strength of Concrete
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An ANN Model for Predicting the Compressive Strength of Concrete

Lin, Chia-Ju ; Wu, Nan-Jing

Applied sciences, 2021-05, Vol.11 (9), p.3798 [Periódico revisado por pares]

Basel: MDPI AG

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