@article{(Open Science Index):https://publications.waset.org/pdf/14131,
	  title     = {Modeling of Reinforcement in Concrete Beams Using Machine Learning Tools},
	  author    = {Yogesh Aggarwal},
	  country	= {},
	  institution	= {},
	  abstract     = {The paper discusses the results obtained to predict
reinforcement in singly reinforced beam using Neural Net (NN),
Support Vector Machines (SVM-s) and Tree Based Models. Major
advantage of SVM-s over NN is of minimizing a bound on the
generalization error of model rather than minimizing a bound on
mean square error over the data set as done in NN. Tree Based
approach divides the problem into a small number of sub problems to
reach at a conclusion. Number of data was created for different
parameters of beam to calculate the reinforcement using limit state
method for creation of models and validation. The results from this
study suggest a remarkably good performance of tree based and
SVM-s models. Further, this study found that these two techniques
work well and even better than Neural Network methods. A
comparison of predicted values with actual values suggests a very
good correlation coefficient with all four techniques.},
	    journal   = {International Journal of Civil and Environmental Engineering},
	  volume    = {1},
	  number    = {8},
	  year      = {2007},
	  pages     = {69 - 73},
	  ee        = {https://publications.waset.org/pdf/14131},
	  url   	= {https://publications.waset.org/vol/8},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 8, 2007},
	}