The overuse level of cement and natural sand for civil industry has several undesira- ble social and ecological consequences. As an answer for this, industrial wastes or by- products (pozzolanic materials) such as fly ash, GGBFS, silica fume and metakaolin can be used to interchange partially cement and natural sand by manufacturing sand (M-sand). In this study, Artificial Neural Networks (ANNs) models were developed for predicting the tensile strength, at the age of 28 days, of concretes containing partly pozzolanic materials and partly replacing natural sand by manufactured sand. Tensile strength test were performed and test results were used to construct ANN model. A total of 131 values was used for modeling ANN, 80% in the training phase, and 20% in the testing phase. To construct the model, 25 input parameters were used to achieve one output parameter, referred to as the tensile strength of concrete con- taining partly pozzolanic materials and manufactured sand. The results obtained in both, the training and testing phases strongly show the potential use of ANN to pre- dict 28 days tensile strength of concretes containing partly pozzolanic materials and manufactured sand.
Agrawal V, Sharma A (2010). Prediction of slump in concrete using ar- tificial neural networks. International Journal of Civil and Environ- mental Engineering, 4(9), 279-286.
Ashrafi HR, Jalal M, Garmsiri K (2017). Prediction of compressive strength of composite fibers reinforced concrete (FRC) using artifi- cial neural network. Proceedings of 3 rd International Conference on Concrete and Development, 824-830.
Boukhatem B, Kenai S, Hamou AT, Ziou Dj, Ghrici M (2017). Prediction concrete properties using neural network (NN) with principal compo- nent analysis (PCA) technique. Computers and Concrete, 10(6), 1-17.
Dantas ATA, Leite MB, Nagahama KJ (2013). Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks. Construction and Building Materi- als, 38, 717-722.
Goyal PK, Prajapati R (2017). Prediction of compressive strength of con- crete using artificial neural network: A case study. International Jour- nal of Engineering Technology Science and Research, 4(3), 276-280.
Islam MN, Zain MF, Jamil M (2012). Prediction of strength and slump of rice husk ash incorporated high-performance concrete. Journal of Civil Engineering and Management, 18(3), 310-317.
Khademi F, Behfarnia K (2017). Evaluation of concrete compressive strength using artificial network and multiple linear regression models. International Journal of Optimization in Civil Engineering, 6(3), 423-432.
Khademi F, Jamal SM, Deshpande N, Londhe S (2016). Predicting strength of recycled aggregate concrete using artificial neural net- work, adaptive neuro-fuzzy Inference system and multiple linear regression. International Journal of Sustainable Built Environment, 5, 355-369.
Magudeaswaran P, Eswaramoorthi P (2016). High performance con- crete using M sand. Asian Journal of Research in Social Sciences and Humanities, 6(6), 372-386.
Mouli M (2008) Performance characteristics of light weight aggregate concrete containing natural pozzolans. Building and Environments, 43(1), 31-36.
Najigivi A, Khaloo A, Iraji zad A, Abdul Rashid (2013). An artificial neu- ral networks model for predicting permeability properties of nano silica–rice husk ash ternary blended concrete. International Journal of Concrete Structures and Materials, 7(3), 225-238.
Ni HG, Wang JZ (2000). Prediction of compressive strength of concrete by neural networks. Cement and Concrete Research, 30(8), 1245-1250.
Reddy TCS (2018) Prediction the strength of slurry infiltrated fibrous concrete using artificial neural network. Frontiers of Structural and Civil Engineering, 12(4), 490-503.
Safiuddin M, Raman SN, Abdus Salam M, Jumaat MZ (2016). Modeling of compressive strength for self-consolidating high-strength concrete incorporating palm oil fuel ash. Materials (MPDI), 9(396), 2-13.
Sayed-Ahmed M (2012). Statistical modelling and prediction of compres- sive strength of concrete. Concrete Research Letters, 3(2), 452-458.
Vignesh SB, Alisha BB, Karthik, Pai S, Prasad S (2016). Prediction of com- pressive strength of concrete by artificial neural network. Interna- tional Journal of Informative & Futuristic Research, 3(9), 3385-3397.
Yalley PP, Sam A (2018). Effect of sand fines and water/cement ratio on concrete properties. Civil Engineering Research Journal, 4(3), 1-7.