Compressive strength prediction of SCC containing fly ash using SVM and PSO-SVM models
By: Rajeshwari, R
.
Contributor(s): Mandal, Sukomal | Rajasekaran, C.
Publisher: Chennai CSIR- Strctural Engineering research Centre 2021Edition: Vol.48, Issue 1.Description: 1-11p.Subject(s): Civil Engineering | Fly Ash | Support Vector Regression | Particle Swarm Optimization
In:
Journal of structural engineering (JOSE)Summary: Self-Compacting Concrete (SCC), is a highly workable material,
compacted by its self weight without observable segregation and
bleeding. In this study, Support Vector Machine (SVM) and particle
swarm optimization based SVM models are employed to predict the
28 days compressive strength of individual SCC mix. A database of 62
no’s of SCC compressive strength from literature with cement partially
replaced by fly ash is used for training the models. The test data
consists of two groups, an individual study consisting of 9 datasets
and other combination of three studies with 19 datasets tested
separately. Similar input parameters from the train data is extended for
testing the models prediction accuracy. Statistical parameters such as
correlation coefficient, root mean square error and scatter index are
used to evaluate the models’ prediction results. The particle swarm
optimization based SVM model is capable of selecting appropriate
SVM parameters to increase the prediction accuracy. From the results,
it is seen that both SVM and particle swarm optimized SVM models
have good capability in predicting the SCC compressive strength.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
|
School of Engineering & Technology (PG) Archieval Section | Not for loan | 2022-0375 |
Self-Compacting Concrete (SCC), is a highly workable material,
compacted by its self weight without observable segregation and
bleeding. In this study, Support Vector Machine (SVM) and particle
swarm optimization based SVM models are employed to predict the
28 days compressive strength of individual SCC mix. A database of 62
no’s of SCC compressive strength from literature with cement partially
replaced by fly ash is used for training the models. The test data
consists of two groups, an individual study consisting of 9 datasets
and other combination of three studies with 19 datasets tested
separately. Similar input parameters from the train data is extended for
testing the models prediction accuracy. Statistical parameters such as
correlation coefficient, root mean square error and scatter index are
used to evaluate the models’ prediction results. The particle swarm
optimization based SVM model is capable of selecting appropriate
SVM parameters to increase the prediction accuracy. From the results,
it is seen that both SVM and particle swarm optimized SVM models
have good capability in predicting the SCC compressive strength.
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