Abstract
Predicting properties of concrete is a major issue for building sustainable structures. In the last decade, numerous publications have shown that machine learning algorithms can play a major role to predict these properties. The key factor is the availability of data to train the models. Collecting, cleaning and consolidating data can be challenging tasks, especially in a concrete industry in which the digitalization of the supply chain is still in progress. We propose in this study to use raw production data to evaluate the performance of a machine learning algorithm compared to an empirical model. The concrete strength value is predicted using both approaches and compared to the measured value. Even if machine learning algorithm shows good performance, no significant increase in the prediction accuracy is obtained.
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Delaplace, A., Razinhas, U.O., Bouchard, R., Griesser, A. (2023). Performance of Data Driven Algorithms to Predict Concrete Strength Using Production Raw Data. In: Jędrzejewska, A., Kanavaris, F., Azenha, M., Benboudjema, F., Schlicke, D. (eds) International RILEM Conference on Synergising Expertise towards Sustainability and Robustness of Cement-based Materials and Concrete Structures. SynerCrete 2023. RILEM Bookseries, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-031-33187-9_64
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DOI: https://doi.org/10.1007/978-3-031-33187-9_64
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