Compressive strength prediction of SCC containing fly ash using SVM and PSO-SVM models (Record no. 16231)
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| 000 -LEADER | |
|---|---|
| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20221201120348.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220205b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 15874 |
| Author | Rajeshwari, R. |
| 245 ## - TITLE STATEMENT | |
| Title | Compressive strength prediction of SCC containing fly ash using SVM and PSO-SVM models |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.48, Issue 1 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | CSIR- Strctural Engineering research Centre |
| Year | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 1-11p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4621 |
| Topical term or geographic name entry element | Civil Engineering |
| 653 ## - Keywords | |
| Keywords | Fly Ash |
| 653 ## - Keywords | |
| Keywords | Support Vector Regression |
| 653 ## - Keywords | |
| Keywords | Particle Swarm Optimization |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 15875 |
| Co-Author | Mandal, Sukomal |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 15019 |
| Co-Author | Rajasekaran, C |
| 773 0# - HOST ITEM ENTRY | |
| International Standard Serial Number | 0970-0137 |
| Place, publisher, and date of publication | Chennai CSIR-Structural Engineering Research Centre |
| Title | Journal of structural engineering (JOSE) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Date acquired | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| School of Engineering & Technology (PG) | School of Engineering & Technology (PG) | Archieval Section | 2022-02-05 | 2022-0375 | 2022-02-05 | 2022-02-05 | Articles Abstract Database |