Prediction of maximum local scour is necessary for
the safety and economical design of the bridges. A number of
equations have been developed over the years to predict local scour
depth using laboratory data and a few pier equations have also been
proposed using field data. Most of these equations are empirical in
nature as indicated by the past publications. In this paper attempts
have been made to compute local depth of scour around bridge pier in
dimensional and non-dimensional form by using linear regression,
simple regression and SVM (Poly & Rbf) techniques along with few
conventional empirical equations. The outcome of this study suggests
that the SVM (Poly & Rbf) based modeling can be employed as an
alternate to linear regression, simple regression and the conventional
empirical equations in predicting scour depth of bridge piers. The
results of present study on the basis of non-dimensional form of
bridge pier scour indicate the improvement in the performance of
SVM (Poly & Rbf) in comparison to dimensional form of scour.
[1] G. W. Parker, L. Bratton, and, D. S. Armstrong, “Stream stability and
scour assessments at bridges in Massachusetts,” U.S. Geological Survey
Open File Report No. 97-588 (CD-ROM), Massachusetts Highway Dept.
Bridge Section, Marlborough, Mass., Rep. .53, 1997.
[2] D. M. Sheppard, B. Melville, and H. Demir, “Evaluation of existing
equations for local scour at bridge piers.” Journal of Hydraulic Engg.,
2014,140(01), pp 14-23.
[3] T. L. Lee, D.S. Jeng and J.H. Hong, “Neural network modeling for scour
depth around bridge piers,” Journal of Hydrodynamics, Vol. 19(3), pp.
378-386, 2007.
[4] Lu Deng and C.S. Cai, “Bridge scour; prediction, modelling, monitoring,
and countermeasures-review,” Journal of Practice periodical structural
design and construction, ASCE, Vol. 15(2), pp. 125-134, 2010.
[5] R. E. Ettema, B.W. Melville and B. Barkdoll, “Closure. A scale effect in
pier-scour experiments,” Journal of Hydraulic Engineering, ASCE, Vol.
125(8), pp.895-896, 1999.
[6] E. M. Laursen and A. Toch, “Scour around bridge piers and abutments,”
Iowa Highway Research Board, Vol. 4, pp. 60, 1956.
[7] H. W. Shen (1971). Scour near piers. In: River Mechanics, II, Chap. 23,
Ft. Collins, Colo.
[8] Breusers, H. N. C., Nicollet, G., and Shen, H. W. “Local scour around
cylindrical piers.” Journal of Hydraulcs Research, 15 (3), 211-252,
1977.
[9] Hancu, S. Sur le calcul des affouillements locaux dams la zone des piles
des ponts. Proc., 14th IAHR Congress, Paris, France, 3,. 299-313,1971.
[10] U.S. DOT. “Evaluating scour at bridges”. Hydraul. Eng. Circular No.18,
FHWA-IP-90- 017, Fed. Hwy. Admin., U.S. Dept. of Transp., McLean,
Va.1993. [11] M. Pal, N. K.Singh, and N. K. Tiwari, “Support vector regression based
modeling of pier scour using field data”, Journal of Engineer
Applications of Artificial Intelligence, 24(5), 911-916, 2011.
[12] J. Hong, M. Goyal, Y. Chiew, and L. Chua, “Predicting time-dependent
pier scour depth with support vector regression”, Journal of Hydrology,
468-469, 241-248, 2012.
[13] Mahesh Pal, N.K. Singh and N.K. Tiwari, “Pier scour modelling using
random forest regression”. ISH Journal of Hydraulic Engineering,
Volume 19, Issue 2, pages 69-75, 2013.
[14] M. Pal, N. K. Singh, and N. K. Tiwari, “Kernel methods for pier scour
modeling using field data ” Journal of Hydroinformatics, Vol. 16, No. 4,
pp 784–796,2014.
[15] I. Kim, M. Fard, and A. Chattopadhyay, “Investigation of a Bridge Pier
Scour Prediction Model for Safe Design and Inspection”. Journal of
Bridge Engg, 10.1061/(ASCE)BE.1943-5592.0000677,04014088.just
released,2014.
[16] Min-Yuan Cheng and Minh-Tu Cao, “Hybrid intelligent inference model
for enhancing prediction accuracy of scour depth around bridge piers”.
Journal of Structure and Infrastructure Engineering: Maintenance,
Management, Life-Cycle Design and Performance.
ttp://dx.doi.org/10.1080/15732479.2014.939089,2014.
[17] Vapnik, V. N., The Nature of Statistical Learning Theory. New York:
Springer-Verlag 1995.
[18] Vapnik, V. N. Statistical Learning Theory. New York: John Wiley and
Sons, 1998.
[19] Smola, A. J. Regression estimation with support vector learning
machines. Master’s Thesis, Technische Universität München, Germany,
1996.
[20] Cortes, C., and Vapnik, V. N. Support Vector Networks, Machine
Learning. 20, pp 273-297, 1995.
[21] Leunberger, D,. Linear and Nonlinear Programming. Addison-Wesley,
1984.
[22] U. C. Kothyari, R. J. Garde and K. G. Range Raju, “Temporal variation
of scour around circular bridge piers,” J. Hydr. Eng., Vol. 118(8), pp.
1091-1106, 1992.
[23] D-S Jeng,S. M. Bateni, E. Lockett," Neural network assessment for
scour depth around bridge piers”, Research Report No R855,
Department of Civil Engineering, Sydney, AUSTRALIA, November,
2005.,http://www.civil.usyd.edu.au/.
[24] Weka software version 3.4.13 http://www.cs.waikato.ac.nz/~ml/weka/.
[1] G. W. Parker, L. Bratton, and, D. S. Armstrong, “Stream stability and
scour assessments at bridges in Massachusetts,” U.S. Geological Survey
Open File Report No. 97-588 (CD-ROM), Massachusetts Highway Dept.
Bridge Section, Marlborough, Mass., Rep. .53, 1997.
[2] D. M. Sheppard, B. Melville, and H. Demir, “Evaluation of existing
equations for local scour at bridge piers.” Journal of Hydraulic Engg.,
2014,140(01), pp 14-23.
[3] T. L. Lee, D.S. Jeng and J.H. Hong, “Neural network modeling for scour
depth around bridge piers,” Journal of Hydrodynamics, Vol. 19(3), pp.
378-386, 2007.
[4] Lu Deng and C.S. Cai, “Bridge scour; prediction, modelling, monitoring,
and countermeasures-review,” Journal of Practice periodical structural
design and construction, ASCE, Vol. 15(2), pp. 125-134, 2010.
[5] R. E. Ettema, B.W. Melville and B. Barkdoll, “Closure. A scale effect in
pier-scour experiments,” Journal of Hydraulic Engineering, ASCE, Vol.
125(8), pp.895-896, 1999.
[6] E. M. Laursen and A. Toch, “Scour around bridge piers and abutments,”
Iowa Highway Research Board, Vol. 4, pp. 60, 1956.
[7] H. W. Shen (1971). Scour near piers. In: River Mechanics, II, Chap. 23,
Ft. Collins, Colo.
[8] Breusers, H. N. C., Nicollet, G., and Shen, H. W. “Local scour around
cylindrical piers.” Journal of Hydraulcs Research, 15 (3), 211-252,
1977.
[9] Hancu, S. Sur le calcul des affouillements locaux dams la zone des piles
des ponts. Proc., 14th IAHR Congress, Paris, France, 3,. 299-313,1971.
[10] U.S. DOT. “Evaluating scour at bridges”. Hydraul. Eng. Circular No.18,
FHWA-IP-90- 017, Fed. Hwy. Admin., U.S. Dept. of Transp., McLean,
Va.1993. [11] M. Pal, N. K.Singh, and N. K. Tiwari, “Support vector regression based
modeling of pier scour using field data”, Journal of Engineer
Applications of Artificial Intelligence, 24(5), 911-916, 2011.
[12] J. Hong, M. Goyal, Y. Chiew, and L. Chua, “Predicting time-dependent
pier scour depth with support vector regression”, Journal of Hydrology,
468-469, 241-248, 2012.
[13] Mahesh Pal, N.K. Singh and N.K. Tiwari, “Pier scour modelling using
random forest regression”. ISH Journal of Hydraulic Engineering,
Volume 19, Issue 2, pages 69-75, 2013.
[14] M. Pal, N. K. Singh, and N. K. Tiwari, “Kernel methods for pier scour
modeling using field data ” Journal of Hydroinformatics, Vol. 16, No. 4,
pp 784–796,2014.
[15] I. Kim, M. Fard, and A. Chattopadhyay, “Investigation of a Bridge Pier
Scour Prediction Model for Safe Design and Inspection”. Journal of
Bridge Engg, 10.1061/(ASCE)BE.1943-5592.0000677,04014088.just
released,2014.
[16] Min-Yuan Cheng and Minh-Tu Cao, “Hybrid intelligent inference model
for enhancing prediction accuracy of scour depth around bridge piers”.
Journal of Structure and Infrastructure Engineering: Maintenance,
Management, Life-Cycle Design and Performance.
ttp://dx.doi.org/10.1080/15732479.2014.939089,2014.
[17] Vapnik, V. N., The Nature of Statistical Learning Theory. New York:
Springer-Verlag 1995.
[18] Vapnik, V. N. Statistical Learning Theory. New York: John Wiley and
Sons, 1998.
[19] Smola, A. J. Regression estimation with support vector learning
machines. Master’s Thesis, Technische Universität München, Germany,
1996.
[20] Cortes, C., and Vapnik, V. N. Support Vector Networks, Machine
Learning. 20, pp 273-297, 1995.
[21] Leunberger, D,. Linear and Nonlinear Programming. Addison-Wesley,
1984.
[22] U. C. Kothyari, R. J. Garde and K. G. Range Raju, “Temporal variation
of scour around circular bridge piers,” J. Hydr. Eng., Vol. 118(8), pp.
1091-1106, 1992.
[23] D-S Jeng,S. M. Bateni, E. Lockett," Neural network assessment for
scour depth around bridge piers”, Research Report No R855,
Department of Civil Engineering, Sydney, AUSTRALIA, November,
2005.,http://www.civil.usyd.edu.au/.
[24] Weka software version 3.4.13 http://www.cs.waikato.ac.nz/~ml/weka/.
@article{"International Journal of Architectural, Civil and Construction Sciences:71275", author = "Arun Goel", title = "Predicting Bridge Pier Scour Depth with SVM", abstract = "Prediction of maximum local scour is necessary for
the safety and economical design of the bridges. A number of
equations have been developed over the years to predict local scour
depth using laboratory data and a few pier equations have also been
proposed using field data. Most of these equations are empirical in
nature as indicated by the past publications. In this paper attempts
have been made to compute local depth of scour around bridge pier in
dimensional and non-dimensional form by using linear regression,
simple regression and SVM (Poly & Rbf) techniques along with few
conventional empirical equations. The outcome of this study suggests
that the SVM (Poly & Rbf) based modeling can be employed as an
alternate to linear regression, simple regression and the conventional
empirical equations in predicting scour depth of bridge piers. The
results of present study on the basis of non-dimensional form of
bridge pier scour indicate the improvement in the performance of
SVM (Poly & Rbf) in comparison to dimensional form of scour.", keywords = "Modeling, pier scour, regression, prediction, SVM
(Poly & Rbf kernels).", volume = "9", number = "2", pages = "211-6", }
