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PLS Application to Optimize the Formulation of an Eco-Geo-Material Based on a Multivariate Response

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Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling (Uncertainties 2020)

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Abstract

The ecological and environmental issues today encourage the use of eco-geo-materials that consume less «grey energy» , such as raw earth building material. This raw earth material is inexpensive and is generally available on the construction site. The compressive strength and ductility have a major role on its mechanical behavior. However, few studies are effectively dedicated to estimate both the compressive strength and the ductility property of raw earth materials. Treated with a very low percentage of binders, the raw earth is transformed into an eco-material suitable for use in the construction. However, the experimental study of the raw earth concrete mechanical properties presents uncertainties related to the bio-sourced nature of its components, such as plant fibers and raw earth. In this study, the raw earth samples of 25 different formulations were casted and tested for 90 days of curing time. The samples were tested for unconfined compressive strength through which ductility index were inferred. In order to optimize the formulation of this raw earth material, a «Design of Experiments» was conducted to study the effect of the various components on these two mechanical properties. A multivariate statistical regression technique of PLS, Partial Least Square, was performed to evaluate the design. This PLS technique was selected because of the complicated experimental design data along with different constraints on model based on the two responses. The obtained results show this technique could be a helpful tool to improve and optimize a raw earth concrete formulation.

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Correspondence to Saber Imanzadeh .

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Imanzadeh, S., Jarno, A., Taibi, S. (2021). PLS Application to Optimize the Formulation of an Eco-Geo-Material Based on a Multivariate Response. In: De Cursi, J. (eds) Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling. Uncertainties 2020. Lecture Notes in Mechanical Engineering(). Springer, Cham. https://doi.org/10.1007/978-3-030-53669-5_20

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  • DOI: https://doi.org/10.1007/978-3-030-53669-5_20

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