Surrogate models for the compressive strength mapping of cement mortar materials

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

Risultato della ricerca: Articlepeer review

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.
Lingua originaleEnglish
pagine (da-a)6347-6372
Numero di pagine26
RivistaSoft Computing
Volume25
Stato di pubblicazionePublished - 2021

All Science Journal Classification (ASJC) codes

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  • ???subjectarea.asjc.1700.1712???
  • ???subjectarea.asjc.2600.2608???

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