Prediction of compressive strength of concrete: machine learning approaches

Dipro Dutta, Sudhirkumar V. Barai

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Abrams’ law is commonly used to predict the compressive strength of concrete with respect to the water content of the mix, but it is largely inaccurate. High-performance concrete, with its complex additional ingredients, makes the prediction more difficult. The goal of the paper is to find the most accurate model for prediction of the compressive strength of a given concrete mix using machine learning (ML). First, the various ML models are explained along with their working principles. Second, the evaluation methods used for the error analysis in the study are discussed. Third, the findings of the study are displayed and inferences are drawn from them. It is found that the 2-nearest-neighbour performs the best with an error of 8.5% and a standard deviation of 1.55.

Original languageEnglish
Title of host publicationLecture Notes in Civil Engineering
PublisherSpringer Paris
Pages503-513
Number of pages11
DOIs
Publication statusPublished - 01-01-2019
Externally publishedYes

Publication series

NameLecture Notes in Civil Engineering
Volume11
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Fingerprint

Compressive strength
Learning systems
Concretes
High performance concrete
Concrete mixtures
Error analysis
Water content

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering

Cite this

Dutta, D., & Barai, S. V. (2019). Prediction of compressive strength of concrete: machine learning approaches. In Lecture Notes in Civil Engineering (pp. 503-513). (Lecture Notes in Civil Engineering; Vol. 11). Springer Paris. https://doi.org/10.1007/978-981-13-0362-3_40
Dutta, Dipro ; Barai, Sudhirkumar V. / Prediction of compressive strength of concrete : machine learning approaches. Lecture Notes in Civil Engineering. Springer Paris, 2019. pp. 503-513 (Lecture Notes in Civil Engineering).
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Dutta, D & Barai, SV 2019, Prediction of compressive strength of concrete: machine learning approaches. in Lecture Notes in Civil Engineering. Lecture Notes in Civil Engineering, vol. 11, Springer Paris, pp. 503-513. https://doi.org/10.1007/978-981-13-0362-3_40

Prediction of compressive strength of concrete : machine learning approaches. / Dutta, Dipro; Barai, Sudhirkumar V.

Lecture Notes in Civil Engineering. Springer Paris, 2019. p. 503-513 (Lecture Notes in Civil Engineering; Vol. 11).

Research output: Chapter in Book/Report/Conference proceedingChapter

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Dutta D, Barai SV. Prediction of compressive strength of concrete: machine learning approaches. In Lecture Notes in Civil Engineering. Springer Paris. 2019. p. 503-513. (Lecture Notes in Civil Engineering). https://doi.org/10.1007/978-981-13-0362-3_40