Abstract
Accurate forecasting of compressive strength (fc) is one of the important disputes in the concrete industry. Empirical and mathematical models, such as linear and nonlinear regression, have been established. However, these models demand hard empirical work to evolve and can contribute erroneous results when the relationships between actual properties and mixture composition and curing conditions are complex. Several machine learning (ML) models of artificial intelligence overcome such disadvantage in foreseeing the properties of concrete. An Artificial Neural Network (ANN) model, run in a MATLAB platform, for predicting the compressive strength of concrete is established in view of this study. The back-propagation (BP) network with multiple hidden layer is preferred as the structure of the ANN. For assembling the model, a dataset of empirical data was taken from an exploratory research and used for training and testing the model. Finally, the suggested model was validated by the way of the collections’ dataset of prior studies.
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Amruthamol, N.A., Kapoor, K. (2023). Machine Learning Model to Forecast Concrete Compressive Strength. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 522. Springer, Singapore. https://doi.org/10.1007/978-981-19-5292-0_12
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DOI: https://doi.org/10.1007/978-981-19-5292-0_12
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