### 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 language | English |
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Title of host publication | Lecture Notes in Civil Engineering |

Publisher | Springer Paris |

Pages | 503-513 |

Number of pages | 11 |

DOIs | |

Publication status | Published - 01-01-2019 |

Externally published | Yes |

### Publication series

Name | Lecture Notes in Civil Engineering |
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Volume | 11 |

ISSN (Print) | 2366-2557 |

ISSN (Electronic) | 2366-2565 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Civil and Structural Engineering

### Cite this

*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

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*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.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Prediction of compressive strength of concrete

T2 - machine learning approaches

AU - Dutta, Dipro

AU - Barai, Sudhirkumar V.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85060354823&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85060354823&partnerID=8YFLogxK

U2 - 10.1007/978-981-13-0362-3_40

DO - 10.1007/978-981-13-0362-3_40

M3 - Chapter

AN - SCOPUS:85060354823

T3 - Lecture Notes in Civil Engineering

SP - 503

EP - 513

BT - Lecture Notes in Civil Engineering

PB - Springer Paris

ER -