000			a
999			_c16231 _d16231
003			OSt
005			20221201120348.0
008			220205b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_915874 _aRajeshwari, R.
245			_aCompressive strength prediction of SCC containing fly ash using SVM and PSO-SVM models
250			_aVol.48, Issue 1
260			_aChennai _bCSIR- Strctural Engineering research Centre _c2021
300			_a1-11p.
520			_aSelf-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	_94621 _aCivil Engineering
653			_aFly Ash
653			_aSupport Vector Regression
653			_aParticle Swarm Optimization
700			_915875 _aMandal, Sukomal
700			_915019 _aRajasekaran, C
773	0		_x0970-0137 _dChennai CSIR-Structural Engineering Research Centre _tJournal of structural engineering (JOSE)
942			_2ddc _cAR