A Comparative Analysis of Machine Learning Models in Prediction of Mortar Compressive Strength

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F22%3A10250061" target="_blank" >RIV/61989100:27230/22:10250061 - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000831902300001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000831902300001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/pr10071387" target="_blank" >10.3390/pr10071387</a>

Result language

angličtina

Original language name

A Comparative Analysis of Machine Learning Models in Prediction of Mortar Compressive Strength

Original language description

Predicting the mechanical properties of cement-based mortars is essential in understanding the life and functioning of structures. Machine learning (ML) algorithms in this regard can be especially useful in prediction scenarios. In this paper, a comprehensive comparison of nine ML algorithms, i.e., linear regression (LR), random forest regression (RFR), support vector regression (SVR), AdaBoost regression (ABR), multi-layer perceptron (MLP), gradient boosting regression (GBR), decision tree regression (DT), hist gradient boosting regression (hGBR) and XGBoost regression (XGB), is carried out. A multi-attribute decision making method called TOPSIS (technique for order of preference by similarity to ideal solution) is used to select the best ML metamodel. A large dataset on cement-based mortars consisting of 424 sample points is used. The compressive strength of cement-based mortars is predicted based on six input parameters, i.e., the age of specimen (AS), the cement grade (CG), the metakaolin-to-total-binder ratio (MK/B), the water-to-binder ratio (W/B), the superplasticizer-to-binder ratio (SP) and the binder-to-sand ratio (B/S). XGBoost regression is found to be the best ML metamodel while simple metamodels like linear regression (LR) are found to be insufficient in handling the non-linearity in the process. This mapping of the compressive strength of mortars using ML techniques will be helpful for practitioners and researchers in identifying suitable mortar mixes.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20300 - Mechanical engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Processes

ISSN

2227-9717

e-ISSN

2227-9717

Volume of the periodical

10

Issue of the periodical within the volume

7

Country of publishing house

CH - SWITZERLAND

Number of pages

16

Pages from-to

nestrankovano

UT code for WoS article

000831902300001

EID of the result in the Scopus database

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