Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag

Concrete production by replacing cement with green materials has been conducted in recent years considering the strategy of sustainable development. This study researched the topic of compressive strength regarding one type of green concrete containing blast furnace slag. Although some researchers h...

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Bibliographic Details
Published in:Materials 2022-06, Vol.15 (13), p.4582
Main Authors: Wu, Xiangping, Zhu, Fei, Zhou, Mengmeng, Sabri, Mohanad Muayad Sabri, Huang, Jiandong
Format: Article
Language:English
Subjects:
Algorithms
Antennae
Artificial intelligence
Artificial neural networks
Back propagation
Back propagation networks
Blast furnace slags
Cement
Comparative studies
Compressive strength
Concrete
Construction materials
Correlation analysis
Decision trees
Environmental impact
Laboratories
Machine learning
Mechanical properties
Neural networks
Optimization
Search algorithms
Slag
Support vector machines
Sustainable development
Test methods
Variables
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Summary:Concrete production by replacing cement with green materials has been conducted in recent years considering the strategy of sustainable development. This study researched the topic of compressive strength regarding one type of green concrete containing blast furnace slag. Although some researchers have proposed using machine learning models to predict the compressive strength of concrete, few researchers have compared the prediction accuracy of different machine learning models on the compressive strength of concrete. Firstly, the hyperparameters of BP neural network (BPNN), support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor algorithm (KNN), logistic regression (LR), and multiple linear regression (MLR) are tuned by the beetle antennae search algorithm (BAS). Then, the prediction effects of the above seven machine learning models on the compressive strength of concrete are evaluated and compared. The comparison results show that KNN has higher R values and lower RSME values both in the training set and test set; that is, KNN is the best model for predicting the compressive strength of concrete among the seven machine learning models mentioned above.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma15134582