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,<br/>compacted by its self weight without observable segregation and<br/>bleeding. In this study, Support Vector Machine (SVM) and particle<br/>swarm optimization based SVM models are employed to predict the<br/>28 days compressive strength of individual SCC mix. A database of 62<br/>no’s of SCC compressive strength from literature with cement partially<br/>replaced by fly ash is used for training the models. The test data<br/>consists of two groups, an individual study consisting of 9 datasets<br/>and other combination of three studies with 19 datasets tested<br/>separately. Similar input parameters from the train data is extended for<br/>testing the models prediction accuracy. Statistical parameters such as<br/>correlation coefficient, root mean square error and scatter index are<br/>used to evaluate the models’ prediction results. The particle swarm<br/>optimization based SVM model is capable of selecting appropriate<br/>SVM parameters to increase the prediction accuracy. From the results,<br/>it is seen that both SVM and particle swarm optimized SVM models<br/>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 | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology (PG) | School of Engineering & Technology (PG) | Archieval Section | 05/02/2022 | 2022-0375 | 05/02/2022 | 05/02/2022 | Articles Abstract Database |