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Soft Computing

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25-02-2021 | Methodologies and Application

Surrogate models for the compressive strength mapping of cement mortar materials

Authors: Panagiotis G. Asteris, Liborio Cavaleri, Hai-Bang Ly, Binh Thai Pham

Published in: Soft Computing | Issue 8/2021

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Abstract

Despite the extensive use of mortar materials in constructions over the last decades, there is not yet a robust quantitative method available in the literature, which can reliably predict their strength based on the mix components. This limitation is attributed to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques for predicting the compressive strength of mortars is investigated. Specifically, Levenberg–Marquardt, biogeography-based optimization, and invasive weed optimization algorithms are used for this purpose (based on experimental data available in the literature). The comparison of the derived results with the experimental findings demonstrates the ability of artificial intelligence techniques to approximate the compressive strength of mortars in a reliable and robust manner.

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Title: Surrogate models for the compressive strength mapping of cement mortar materials
Authors: Panagiotis G. Asteris
Liborio Cavaleri
Hai-Bang Ly
Binh Thai Pham
Publication date: 25-02-2021
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 8/2021
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-021-05626-3

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