Concrete strength and durability prediction through deep learning and artificial neural networks

Abstract

The mechanical and durability characteristics of concrete are crucial for designing and evaluating concrete structures throughout their entire operational lifespan. The main objective of this research is to use the deep learning (DL) method along with an artificial neural network (ANN) to predict the chloride migration coefficient and concrete compressive strength. An expansive experimental database of nearly 1100 data points was gathered from existing scientific literature. Four forecast models were created, utilizing between 10 and 12 input features. The ANN was used to address the missing data gaps in the literature. A comprehensive pre-processing approach was then implemented to identify outliers and encode data attributes. The use of mean absolute error (MAE) as an evaluation metric for regression tasks and the employment of a confusion matrix for classification tasks were found to produce accurate results. Additionally, both the compressive strength and chloride migration coefficient exhibit a high level of accuracy, above 0.85, in both regression and classification tasks. Moreover, a user-friendly web application was successfully developed in the present study using the Python programming language, improving the ability to integrate smoothly with the user’s device.

Graphical abstract

Keywords

chloride migration coefficient / compressive strength / concrete / artificial neural network / deep learning

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Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11709-024-1124-9 and is accessible for authorized users.

Competing interests

The authors declare that they have no competing interests.

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