7ce5ed77-d217-4b50-a055-9ea8bb6f7548 beie director@blueeyesintelligence.org 10.35940/ijrte.B7738.0712223 10.1016/S0008-8846(98)00165-3Yeh, I.-C. Modeling of strength of high-performance concrete using artificial neural networks. Cem. Concr. Res. 1998, 28, 1797-1808. [CrossRef]10.1016/S0141-0296(03)00004-XLee, S.-C. Prediction of concrete strength using artificial neural net-works. Eng. Struct. 2003, 25, 849-857. [CrossRef]10.1016/j.conbuildmat.2018.05.201Bui, D.-K.; Nguyen, T.; Chou, J.-S.; Nguyen-Xuan, H.; Ngo, T.D. A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Constr. Build. Mater. 2018, 180, 320-333. [CrossRef]MaterialsAlghamdi1551950202210.3390/ma15051950Classifying High Strength Concrete Mix Design Methods Using Decision TreesAlghamdi, S. J. (2022). Classifying High Strength Concrete Mix Design Methods Using Decision Trees. Materials, 15(5), 1950.‏ [CrossRef]10.1016/j.conbuildmat.2018.04.169Deng, F.; He, Y.; Zhou, S.; Yu, Y.; Cheng, H.; Wu, X. Compressive strength prediction of recycled concrete based on deep learning. Constr. Build. Mater. 2018, 175, 562-569. [CrossRef]Erdal, H.; Erdal, M.; Simsek, O.; Erdal, H.I. Prediction of concrete compressive strength using non-destructive test results. Comp. Concr. 2018, 21, 407-417.Williams, K.C.; Partheeban, P. An experimental and numerical ap-proach in strength prediction of reclaimed rubber concrete. Adv. Concr. Constr. 2018, 6, 87.10.1061/(ASCE)0887-3801(1995)9:4(279)Kasperkiewicz, J.; Racz, J.; Dubrawski, A. HPC strength prediction using artificial neural network. J. Comput. Civ. Eng. 1995, 9, 279-284. [CrossRef]10.1016/S0950-0618(01)00006-XDias, W.; Pooliyadda, S. Neural networks for predicting properties of concretes with admixtures. Constr. Build. Mater. 2001, 15, 371-379. [CrossRef]10.1016/j.conbuildmat.2005.01.054Öztaş, A.; Pala, M.; Özbay, E.A.; Kanca, E.; Caglar, N.; Bhatti, M.A. Predicting the compressive strength and slump of high strength con-crete using neural network. Constr. Build. Mater. 2006, 20, 769-775. [CrossRef]10.1061/(ASCE)MT.1943-5533.0001270Ghafari, E.; Bandarabadi, M.; Costa, H.; Júlio, E. Prediction of fresh and hardened state properties of UHPC: Comparative study of statis-tical mixture design and an artificial neural network model. J. Mater. Civ. Eng. 2015, 27, 04015017. [CrossRef]10.3390/ma15020489Almohammed, F.; Sihag, P.; Sammen, S.S.; Ostrowski, K.A.; Singh, K.; Prasad, C.; Zajdel, P. Assessment of Soft Computing Techniques for the Prediction of Compressive Strength of Bacterial Concrete. Materials 2022, 15, 489. [CrossRef]10.3390/ma14247531Nafees, A.; Javed, M.F.; Khan, S.; Nazir, K.; Farooq, F.; Aslam, F.; Musarat, M.A.; Vatin, N.I. Predictive Modeling of Mechanical Prop-erties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP. Materials 2021, 14, 7531. [CrossRef]10.1016/j.conbuildmat.2008.12.003Alshihri, M.M.; Azmy, A.M.; El-Bisy, M.S. Neural networks for predicting compressive strength of structural light weight concrete. Constr. Build. Mater. 2009, 23, 2214-2219. [CrossRef]10.1016/j.advengsoft.2011.05.016Siddique, R.; Aggarwal, P.; Aggarwal, Y. Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks. Adv. Eng. Softw. 2011, 42, 780-786. [CrossRef]10.1016/j.commatsci.2007.03.010Topcu, I.B.; Sarıdemir, M. Prediction of properties of waste AAC aggregate concrete using artificial neural network. Comput. Mater. Sci. 2007, 41, 117-125. [CrossRef]Sadegh-Azar, H.; Feldbusch, A.; Agne, P.; Kögel, C.: Schwingung-suntersuchungen mit dem Smartphone und Tablet, Bauingenieur, Mai 2017Neural computationLeCun14541198910.1162/neco.1989.1.4.541Backpropagation applied to handwritten zip code recognitionLeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4), 541-551.‏ [CrossRef]In Proceed-ings of the IEEE conference on computer vision and pattern recogni-tion (ppSzegedy201510.1109/cvpr.2015.7298594Going deeper with convolutionsSzegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceed-ings of the IEEE conference on computer vision and pattern recogni-tion (pp. 1-9).‏ [CrossRef]