Prediction Modeling of 28-Day Concrete Compressive Strength using Artificial Neutral Network
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compressive strength
artificial neural network

How to Cite

Uche, O., Abdulwahab, M., Suleiman, A., & Ismail, Y. (2019). Prediction Modeling of 28-Day Concrete Compressive Strength using Artificial Neutral Network. ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY AND ENVIRONMENT, 15(3), 692-701. Retrieved from


Compressive strength of concrete at the age of 28 days is an important parameter for the design of concrete structures and waiting for that length of time to obtain the value can be tasky. This study developed an alternative approach using Artificial Neutral Network (ANN) to estimate or predict the compressive strength of concrete at 28th day from early age results. In the study concrete cubes of mix ratio 1:2:4 were cast with different water-cement ratios (0.4, 0.5, 0.6 and 0.65) and their seventh (7th) and twenty-eighth (28th) day strength were measured in the laboratory. In all, 400 cubes of 150 x 150 x 150mm of 200 sets were subjected to compressive strength test using Avery Denison Universal Testing Machine of 2000 kN load capacity at a constant load application of 15kN/s. ANN model was then developed using the time series tool of ANN in MATLAB 7.12.0 (R2011a) applying back propagation algorithm. Out of the 200 sets of results, 110 sets (55%) were used for the training of the network while 30 sets (15%) were used to validate and 60 sets (30%) to test the network. The result of the crushing test shows that the higher the compressive strength at seventh (7th) day the higher it will be at twenty-eighth (28th) day. The result of the ANN model shows a good correlation between the seventh (7th) day compressive strength and the twenty-eighth (28th) day compressive strength with training and validation correlation coefficients of 0.99751 and 0.99736 respectively. It was also found that the ANN model is quite efficient in determining the twenty-eighth (28th) day compressive strength of concreteas the predicted strength values match very well with those obtained experimentally with a correlation coefficient of 0.99675.

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Copyright of the paper named above is hereby assigned and transferred to the Arid Zone Journal of Engineering, Technology and Environment published by University of Maiduguri, Nigeria.