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Soft computing based formulations for prediction of compressive strength of sustainable concrete: a comprehensive review

Soft computing based formulations for prediction of compressive strength of sustainable concrete:... The desire to grow sustainably has necessitated the utilization of waste materials while producing concrete. The objective of this study is to review the usage of different waste materials in concrete production and to deploy machine learning models to predict the mechanical strength of concrete mixes. This paper focuses on the substitutes available to partially replace sand and cement, the main constituents of concrete. Substitutes from waste materials such as silica fume, metakaolin, fly ash, marble dust, slag, waste foundry exhaust sand, quarry dust, granite slurry, and rice husk ash, and the effect on the mechanical properties of concrete have been explored in this work. Experimental work to determine the quantity of these substitutes in the concrete mixture requires immense resources, time, and money. Therefore, researchers have worked relentlessly to seek out the optimal proportions of substitutes over the last 40 years. Establishment of reliable models to predict the compressive strength of various concrete mixes has been an area of focus for many researchers. The use of machine learning models as an alternative to repetitive field testing has become quite popular in civil engineering lately. Also, this work focusses on different machine learning models used to predict the mechanical properties of different design mixes. This study will act as a resource for researchers and concrete utilizing users to create new machine learning models for predicting the compressive strength of concrete incorporating waste materials and preparing the concrete as per requirement. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Innovative Infrastructure Solutions Springer Journals

Soft computing based formulations for prediction of compressive strength of sustainable concrete: a comprehensive review

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References (162)

Publisher
Springer Journals
Copyright
Copyright © Springer Nature Switzerland AG 2022
ISSN
2364-4176
eISSN
2364-4184
DOI
10.1007/s41062-022-00754-7
Publisher site
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Abstract

The desire to grow sustainably has necessitated the utilization of waste materials while producing concrete. The objective of this study is to review the usage of different waste materials in concrete production and to deploy machine learning models to predict the mechanical strength of concrete mixes. This paper focuses on the substitutes available to partially replace sand and cement, the main constituents of concrete. Substitutes from waste materials such as silica fume, metakaolin, fly ash, marble dust, slag, waste foundry exhaust sand, quarry dust, granite slurry, and rice husk ash, and the effect on the mechanical properties of concrete have been explored in this work. Experimental work to determine the quantity of these substitutes in the concrete mixture requires immense resources, time, and money. Therefore, researchers have worked relentlessly to seek out the optimal proportions of substitutes over the last 40 years. Establishment of reliable models to predict the compressive strength of various concrete mixes has been an area of focus for many researchers. The use of machine learning models as an alternative to repetitive field testing has become quite popular in civil engineering lately. Also, this work focusses on different machine learning models used to predict the mechanical properties of different design mixes. This study will act as a resource for researchers and concrete utilizing users to create new machine learning models for predicting the compressive strength of concrete incorporating waste materials and preparing the concrete as per requirement.

Journal

Innovative Infrastructure Solutions – Springer Journals

Published: Apr 1, 2022

Keywords: Green concrete; Machine learning; Strength prediction; Supplementary cementitious material

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