Corrigendum to “Estimation of concrete materials uniaxial compressive strength using soft computing techniques”(Heliyon, (2023), 9, 11, (e22502), (S2405844023097104), 10.1016/j.heliyon.2023.e22502)

In this article, references [3], [39], [42], [50] and [57] need to be corrected due to contextual inappropriateness: [3] F. Sabry, Translucent Concrete: How-To See-Through Walls? Using Nano Optics and Mixing Fine Concrete and Optical Fibers for Illumination during Day and Night Time, One Billion Knowledgeable, 2022. [39] Y. Song, H. Lee, D. Jung, D. Yoo, M. Park, Development of regression equation for drought occurrence using standard score method: focused on asia, J. Korean Societ. Hazard Mitigat. 19 (7) (2019) 519–527. [42] Z. Šverko, M. Vranki'c, S. Vlahini'c, P. Rogelj, Complex Pearson correlation coefficient for EEG connectivity analysis, Sensors 22 (4) (2022) 1477. [50] Y. Han, C. Li, L. Zheng, G. Lei, L. Li, Remaining useful life prediction of lithium-ion batteries by using a denoising transformer-based neural network, Energies 16 (17) (2023) 6328. [57] H.L. Nguyen, V.Q. Tran, Data-driven approach for investigating and predicting rutting depth of asphalt concrete containing reclaimed asphalt pavement, Contruct. Build. Mater. 377 (2023), 131116. The authors confirm that the references were inadvertently introduced in the article, and the updated references for [3], [39], [42], [50] and [57] reflect the accuracy of the cited sources. The correct version of these references should be as below: [3] M. S. A. Bakar, N. Sa'ude, M. Ibrahim, and N. A. N. Ismail, Additive manufacturing (3D printing): A review of current 3D concrete printing on materials, methods, applications, properties and challenges, AIP Conference Proceedings, 2530(1) (2023) 020001. [39] V. Rathakrishnan, S. Bt. Beddu, and A. N. Ahmed, Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms, Sci. Rep. 12(1) (2022) 9539. [42] H. M. Mohamad et al., A Consistency Check of Concrete Compressive Strength using Pearson's Correlation Coefficient, Civil Eng. J. 7(3) (2021) 541–548. [50] L. Wu, W. Wang, and C. Jiang, Deep learning-based prediction for time-dependent chloride penetration in concrete exposed to coastal environment, Heliyon, 9(6) (2023), e16869. [57] S. S. Pakzad, N. Roshan, and M. Ghalehnovi, "Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete," Sci. Rep. 13(1) (2023) 3646. Also, Equation 2 contained an error:R2=1∑i=1n(Py−O)2∑i=1n(O−O)2 The correct version of Equation 2 should be as below:R2=1−∑i=1n(Py−O)2∑i=1n(O−O‾)2 The authors apologize for the errors.