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Geomechanics and Engineering Volume 33, Number 6, June25 2023 , pages 611-624 DOI: https://doi.org/10.12989/gae.2023.33.6.611 |
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Predicting unconfined compression strength and split tensile strength of soil-cement via artificial neural networks |
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Luís Pereira, Luís Godinho and Fernando G. Branco
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| Abstract | ||
| Soil properties make it attractive as a building material due to its mechanical strength, aesthetically appearance, plasticity, and low cost. However, it is frequently necessary to improve and stabilize the soil mechanical properties with binders. Soil-cement is applied for purposes ranging from housing to dams, roads and foundations. Unconfined compression strength (UCS) and split tensile strength (CD) are essential mechanical parameters for ascertaining the aptitude of soil-cement for a given application. However, quantifying these parameters requires specimen preparation, testing, and several weeks. Methodologies that allowed accurate estimation of mechanical parameters in shorter time would represent an important advance in order to ensure shorter deliverable timeline and reduce the amount of laboratory work. In this work, an extensive campaign of UCS and CD tests was carried out in a sandy soil from the Leiria region (Portugal). Then, using the machine learning tool Neural Pattern Recognition of the MATLAB software, a prediction of these two parameters based on six input parameters was made. The results, especially those obtained with resource to a Bayesian regularization-backpropagation algorithm, are frankly positive, with a forecast success percentage over 90% and very low root mean square error (RMSE). | ||
| Key Words | ||
| artificial neural networks; compression; flexural; mechanical properties; soil-cement | ||
| Address | ||
| Luís Pereira, Luís Godinho and Fernando G. Branco:University of Coimbra, ISISE, ARISE, Department of Civil Engineering, Coimbra, Portugal | ||