2023
DOI: 10.3389/fmats.2023.1159079
|View full text |Cite
|
Sign up to set email alerts
|

Development of hybrid SVM-FA, DT-FA and MLR-FA models to predict the flexural strength (FS) of recycled concrete

Abstract: Recycled concrete from construction waste used as road material is a current sustainable approach. To provide feasible suggestions for civil engineers to prepare recycled concrete with high flexural strength (FS) for the road pavement, the present study proposed three hybrid machine learning models by combining support vector machine (SVM), decision tree (DT) and multiple linear regression (MLR) with the firefly algorithm (FA) for the computational optimization, named as SVM-FA, DT-FA, and MLR-FA, respectively… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2023
2023

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 58 publications
0
1
0
Order By: Relevance
“…Chiya et al [ 30 ] used the curing time of cement paste, water-binder ratio, and the content of microsilica and nanosilica as inputs to predict the mechanical properties of cement paste by constructing a multiple linear regression (MLR) model. Wang et al [ 31 ] used an improved MLR based on Firefly Algorithm (FA) to predict the flexural strength of recycled concrete. After considering nine kinds of concrete preparation parameters, a good regression model was obtained.…”
Section: Introductionmentioning
confidence: 99%
“…Chiya et al [ 30 ] used the curing time of cement paste, water-binder ratio, and the content of microsilica and nanosilica as inputs to predict the mechanical properties of cement paste by constructing a multiple linear regression (MLR) model. Wang et al [ 31 ] used an improved MLR based on Firefly Algorithm (FA) to predict the flexural strength of recycled concrete. After considering nine kinds of concrete preparation parameters, a good regression model was obtained.…”
Section: Introductionmentioning
confidence: 99%