Modeling of Reinforcement in Concrete Beams Using Machine Learning Tools
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.
[1] M. A. Austin and J. L. Preston, "Solid modeling of R.C.Beams:
I. Data Structures and Algorithms" Journal of Computing in
Civil Engineering, ASCE, Vol. 6 (4), pp. 389-403, 1992.
[2] H. M. Chen, K. H. Tsai, G. Z. Qi, J.C.S. Yang, and F. Aminy,
"Neural Network for Structure Control", Journal of Computing
in Civil Engineering, ASCE, Vol. 9 (2), pp. 168-176, 1995.
[3] D. P. Solomatine and K. N. Dulal, "Model trees as an
alternative to neural networks in rainfall-runoff modeling",
Journal of Hydrological Sciences, Vol. 48 (3), pp. 399-411,
2003.
[4] I. H. Witten and E. Frank, Data Mining: Practical Machine
Learning Tools and Techniques with Java Implementation.
Morgan Kaufmann Publisher, 2000.
[5] M. S. Nagesha, P. P. Ramugade, R. Potdar, and S. V. Patil ,
"Rapid estimation of Concrete Strength using predictive tools",
The Indian Concrete Journal, pp. 947-951, 2003.
[6] J. L. Preston and M. A. Austin, "Solid Modeling of R.C.Beams:
II. Computational environment", Journal of Computing in Civil
Engineering, ASCE, Vol. 6 (4), pp. 404-416, 1992.
[7] J. L. Rogers, "Simulating structural analysis with Neural
Network",, Journal of Computing in Civil Engineering, ASCE,
Vol. 8(2), pp. 252-265, 1994.
[8] J. R. Quinlan., "Learning with Continuous Classes",
Proceedings AI-92 (Adams & Sterling), Singapore, World
Scientific, pp. 343-348, 1992.
[9] Y. B. Dibike, S. Celickov, and D. Solomatine, "Support Vector
Machines: Review and Applications in Civil Engineering.2000",
2nd Joint Work Shop on Application of AI in Civil Engineering,
March 2000, Cotbus, Germany, 2000.
[1] M. A. Austin and J. L. Preston, "Solid modeling of R.C.Beams:
I. Data Structures and Algorithms" Journal of Computing in
Civil Engineering, ASCE, Vol. 6 (4), pp. 389-403, 1992.
[2] H. M. Chen, K. H. Tsai, G. Z. Qi, J.C.S. Yang, and F. Aminy,
"Neural Network for Structure Control", Journal of Computing
in Civil Engineering, ASCE, Vol. 9 (2), pp. 168-176, 1995.
[3] D. P. Solomatine and K. N. Dulal, "Model trees as an
alternative to neural networks in rainfall-runoff modeling",
Journal of Hydrological Sciences, Vol. 48 (3), pp. 399-411,
2003.
[4] I. H. Witten and E. Frank, Data Mining: Practical Machine
Learning Tools and Techniques with Java Implementation.
Morgan Kaufmann Publisher, 2000.
[5] M. S. Nagesha, P. P. Ramugade, R. Potdar, and S. V. Patil ,
"Rapid estimation of Concrete Strength using predictive tools",
The Indian Concrete Journal, pp. 947-951, 2003.
[6] J. L. Preston and M. A. Austin, "Solid Modeling of R.C.Beams:
II. Computational environment", Journal of Computing in Civil
Engineering, ASCE, Vol. 6 (4), pp. 404-416, 1992.
[7] J. L. Rogers, "Simulating structural analysis with Neural
Network",, Journal of Computing in Civil Engineering, ASCE,
Vol. 8(2), pp. 252-265, 1994.
[8] J. R. Quinlan., "Learning with Continuous Classes",
Proceedings AI-92 (Adams & Sterling), Singapore, World
Scientific, pp. 343-348, 1992.
[9] Y. B. Dibike, S. Celickov, and D. Solomatine, "Support Vector
Machines: Review and Applications in Civil Engineering.2000",
2nd Joint Work Shop on Application of AI in Civil Engineering,
March 2000, Cotbus, Germany, 2000.
@article{"International Journal of Architectural, Civil and Construction Sciences:63134", author = "Yogesh Aggarwal", title = "Modeling of Reinforcement in Concrete Beams Using Machine Learning Tools", 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.", keywords = "Linear Regression, M5 Model Tree, Neural Network,Support Vector Machines.", volume = "1", number = "8", pages = "81-5", }